Data visualization platform for event-based behavior clustering

ABSTRACT

A platform for processing event traces to generate clusters of journey maps for an interactive user display. The clustering can be implemented using a genetic process. The clustering can be implemented using a distance measures. Other clustering techniques can be used. The platform can use the clustering results to generate interactive dynamic visualizations with interactive selectable portions. The platform can integrate processing mining and journey maps to generate interactive dynamic visualizations.

FIELD

The present disclosure generally relates to the field of datavisualization, machine learning, cluster analysis, and computerinterfaces.

INTRODUCTION

Embodiments described herein relate to a data visualization forevent-based behavior analysis or trace analysis. Machine learning is afield of computer science that involves programming code configured tolearn, adapt or improve performance on a specific task with data,without being explicitly programmed. Cluster analysis can involve codethat processes input data to group a set of objects in clusters based ondetected patterns or similarities. That is, objects grouped in a clusterare more similar (in some sense) to each other than to those in otherclusters. Machine learning can involve cluster analysis to learnpatterns and similarities when processing input data. Machine learningand cluster analysis can generate output data that can be used to createdynamic, interactive visual elements for computer interfaces. Thecomputer interfaces can receive control commands that can be received asinput data and trigger generation of updated visual elements.

SUMMARY

In accordance with an aspect, there is provided a platform forprocessing event traces to generate clusters of the event traces for aninteractive user display.

In various further aspects, the disclosure provides correspondingsystems and devices, and logic structures such as machine-executablecoded instruction sets for implementing such systems, devices, andmethods.

In accordance with an aspect, there is provided a platform with datastorage device having a data warehouse model for storing event traces,each event trace having attributes that indicate activities over time.The platform has a processor configured to process machine executableinstructions to generate visual elements for an interactive interfaceapplication by: generating hierarchical cluster for the event traces bygrouping event traces having similar attributes; generating a set ofrepresentative attributes for a set of event traces of the hierarchicalcluster, the set of representative attributes computed based on thesimilar attributes; generating the visual elements for multiple viewsfor the interface application, the multiple views having a first viewindicating a pattern of activities for the set of representativeattributes; a second view indicating the hierarchical cluster and thepattern of activities within the hierarchical cluster; and a third viewindicating descriptors for the hierarchical cluster, the multiple viewshaving a plurality of selectable portions; controlling rendering of theinterface application at a device to display the multiple viewssynchronously; and responsive to a selection of a selectable portion ofthe plurality of selectable portions, controlling rendering of theinterface application at the device to update the multiple viewssynchronously based on the selected portion to navigate the hierarchicalcluster.

In some embodiments, the processor generates the hierarchical cluster bycomputing a distance measure for the attributes of the event traces tocompute the similar attributes.

In some embodiments, the processor generates the hierarchical cluster bycomputing a distance measure based on a Levenshtein distance.

In some embodiments, the processor generates the hierarchical cluster bycomputing a distance between clusters of the hierarchical cluster.

In some embodiments, the pattern of activities indicates the set ofrepresentative attributes for a segment of the hierarchical cluster thatcorresponds to the set of event traces.

In some embodiments, the second view indicates the segment of thehierarchical cluster for the pattern of activities indicated in thefirst view.

In some embodiments, the selection portion is within a view of themultiple views to trigger an update to the other views of the multipleviews.

In some embodiments, the processor updates the multiple viewssynchronously based on the selected portion to navigate the hierarchicalcluster at different levels of abstraction or granularity.

In some embodiments, the processor generates the hierarchical clusterbased on a proximity measure using an order of activities for the eventtraces.

In some embodiments, the processor computes salient characteristics forthe descriptors to indicate indexes based on a chi-square test.

In some embodiments, the processor receives a navigation goal at theinterface application, the goal indicating an attribute and responsiveto the navigation goal, controlling rendering of the interfaceapplication at the device to update the multiple views synchronously toindicate data based on the navigation goal.

In some embodiments, the processor generates the hierarchical clusterbased on layers, a layer corresponding to a number of event traces thatwill be grouped based on the similar attributes, the layerscorresponding to a height of the hierarchical cluster.

In some embodiments, the processor generates the set of representativeattributes based on the similar attributes and a frequent sequencesmining process.

In accordance with an aspect, there is provided a non-transitorycomputer readable medium storing machine executable instructions toconfigure a processor to: generate hierarchical cluster for the eventtraces by grouping event traces having similar attributes, each eventtrace having attributes that indicate activities over time; generate aset of representative attributes for a set of event traces of thehierarchical cluster, the set of representative attributes computedbased on the similar attributes; generate the visual elements formultiple views for the interface application, the multiple views havinga first view indicating a pattern of activities for the set ofrepresentative attributes; a second view indicating the hierarchicalcluster and the pattern of activities within the hierarchical cluster;and a third view indicating descriptors for the hierarchical cluster,the multiple views having a plurality of selectable portions; controlrendering of the interface application at a device to display themultiple views synchronously; and responsive to a selection of aselectable portion of the plurality of selectable portions, controlrendering of the interface application at the device to update themultiple views synchronously based on the selected portion to navigatethe hierarchical cluster.

In some embodiments, the machine executable instructions configure theprocessor to generate the hierarchical cluster by computing a distancemeasure for the attributes of the event traces to compute the similarattributes.

In some embodiments, the machine executable instructions configure theprocessor to generate the hierarchical cluster by computing a distancebetween clusters of the hierarchical cluster.

In some embodiments, the machine executable instructions configure theprocessor to update the multiple views synchronously based on theselected portion to navigate the hierarchical cluster at differentlevels of abstraction or granularity.

In some embodiments, the machine executable instructions configure theprocessor to receive a navigation goal at the interface application, thegoal indicating an attribute and responsive to the navigation goal,controlling rendering of the interface application at the device toupdate the multiple views synchronously to indicate data based on thenavigation goal.

In some embodiments, the machine executable instructions configure theprocessor to generate the hierarchical cluster based on layers, a layercorresponding to a number of event traces that will be grouped based onthe similar attributes, the layers corresponding to a height of thehierarchical cluster.

In accordance with an aspect, there is provided a computer process togenerate visual elements for an interactive interface application. Theprocess involves, at a processor, generating hierarchical cluster forthe event traces by grouping event traces based on distance measures,each event trace having attributes that indicate activities over time,generating a set of representative attributes for a set of event tracesof the hierarchical cluster, the set of representative attributescomputed based on the grouped event traces; generating the visualelements for multiple views for the interface application, the multipleviews having a first view indicating a pattern of activities for the setof representative attributes; a second view indicating the hierarchicalcluster and the pattern of activities within the hierarchical cluster;and a third view indicating descriptors for the hierarchical cluster,the multiple views having a plurality of selectable portions;controlling rendering of the interface application at a device todisplay the multiple views synchronously; and responsive to a selectionof a selectable portion of the plurality of selectable portions,controlling rendering of the interface application at the device toupdate the multiple views synchronously based on the selected portion,wherein the selection portion is within a view of the multiple views totrigger an update to the other views of the multiple views.

In accordance with an aspect, there is provided a platform with a datastorage device for storing event traces, the event traces havingattributes that indicate activities over time. The platform has aprocessor configured to process machine executable instructions togenerate visual elements for an interactive interface application by:computing clusters of the event traces based on a genetic processfunction, each cluster corresponding to a set of event traces and arepresentative event trace based on representative attributes of the setof event traces, the genetic process function mapping each event traceto a cluster; generating the visual elements for an interfaceapplication, the visual elements indicating the clusters and, for eachcluster, the representative event trace based on the representativeattributes, the representative event trace summarizing the set of eventtraces corresponding to the cluster; controlling rendering of theinterface application at a device to display the visual elements and aplurality of selectable portions; and responsive to a selection of aselectable portion of the plurality of selectable portions, controllingrendering of the interface application at the device to navigate theclusters.

In some embodiments, the processor is configured to implement thegenetic process function by: evaluating initial set representative eventtraces to generate elite set representative event traces, generatingadditional initial representative event traces using a transformationprocess, evaluating the additional initial representative event tracesto generate additional elite set representative event traces, continuingthe generating and the evaluating until stopping criterion is met, usingresulting elite representative event traces as the representative eventtraces for the clusters.

In some embodiments, for each event trace, the attributes arerepresented as a two-dimensional tuple of activities over time andcontextual data and the genetic process function can evaluate thetwo-dimensional tuples of the event traces.

In some embodiments, the processor computes, for each cluster, therepresentative event trace as a pattern of activities to summarize thepatterns contained within the set of event traces of the cluster.

In some embodiments, the processor implements the genetic processfunction by pre-processing the event traces, determining an initialpopulation that corresponds to a number of the clusters, evaluatinginitial representative event traces, evaluating stop criteria,implementing a genetic operation to generate additional initialrepresentative event traces, outputting a map abstraction for use ingenerating additional visual elements.

In some embodiments, the processor is configured to evaluate the initialrepresentative event traces based on quality criteria for fitness, anumber of representatives, a contextual distance and an average quality.

In some embodiments, the processor is configured to weigh the qualitycriteria to generate an overall quality and average the overall quality.

In some embodiments, the processor is configured to evaluate the initialrepresentative event traces based on internal and external evaluationmetrics.

In accordance with an aspect, there is provided a platform having a datastorage device for storing event traces, the event traces havingattributes that indicate activities over time, the event traces relatingto current data and historic data. The platform has a processorconfigured to process machine executable instructions to generate visualelements for an interactive interface application by: generating amapping of process mining elements to the event traces, the mappinghaving an XML-based set of concepts, the mapping linking process defacto models and de jure models to actual and expected customerexperiences; generating a data structure for hierarchical components ofa journey map, each component having an element and an attribute, thejourney map having at least one element being a root node of thehierarchical components, the actual and expected customer experiencescorresponding to the journey map; generating a visual elements for thecomponents of the journey map; and transmitting the visual elements toan interface application for display on a device;

In accordance with an aspect, there is provided a platform having a datastorage device for storing event traces, the event traces havingattributes that indicate activities over time. The platform has aprocessor configured to process machine executable instructions togenerate visual elements for a map abstractor of an interactiveinterface application by: generating a process tree for the event tracesby grouping the event traces based on similar attributes, the processtree having nodes corresponding to the event traces, the process treehaving leaf nodes; parsing the process tree; starting from leaf nodes ofthe parsed process tree, iteratively generating a prompt at an interfaceapplication to merge a set of event traces that belong to a subset ofthe parsed process tree; upon receiving confirmation to merge the set ofevent traces, generating a name for the set of event traces andreplacing the nodes corresponding to the set of event traces with a newnode indicating the name and a set of representative attributes for theset of event traces; generating the visual elements for an interfaceapplication, the visual elements indicating the process tree and the newnodes, the visual elements representing an abstracted process tree;controlling rendering of the interface application at a device todisplay the visual elements.

In some embodiments, a process tree is an abstract hierarchicalrepresentation of a process model, where the leaves are annotated withactivities and all the other nodes are annotated with operators.

In this respect, before explaining at least one embodiment in detail, itis to be understood that the embodiments are not limited in applicationto the details of construction and to the arrangements of the componentsset forth in the following description or illustrated in the drawings.Also, it is to be understood that the phraseology and terminologyemployed herein are for the purpose of description and should not beregarded as limiting.

Many further features and combinations thereof concerning embodimentsdescribed herein will appear to those skilled in the art following areading of the instant disclosure.

DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic diagram of platform for clustering and datavisualizations according to some embodiments.

FIG. 2 shows a trace depicting factors influencing vaccinationdecisions.

FIG. 3 shows an example trace model.

FIG. 4 shows the mapping of the XES concepts to a model generated byplatform.

FIG. 5 shows the process mining framework extended for trace models.

FIG. 6 shows the application of process mining activities on trace dataand results.

FIG. 7 shows an example hierarchical clustering that can be generated bythe platform.

FIG. 8 is an example screenshot visible at interface application andpoints out three views that are available to navigate the clusters.

FIG. 9 shows a screenshot of interface application for goal settingimpact on a tree.

FIG. 10 illustrates a cluster implementation containing example steps.

FIG. 11 illustrates a preview of the parameters when uploading adataset.

FIG. 12 is an example screenshot of interface application.

FIG. 13 is a diagram that shows that the process mining framework aswell as the input data are relevant in a customer journey analyticscontext.

FIG. 14 shows that these business process model (BPM) and journey mapsconvey different information.

FIG. 15 depicts phases of determining a representative journey.

FIG. 16 provides an overview of an example genetic process.

FIG. 17 depicts Top from the example given in FIG. 16 .

FIG. 18 illustrates the evolution of the evaluation criteria.

FIG. 19 illustrates a graph of the distribution.

FIG. 20 illustrates a value frequency table.

FIG. 21 illustrates how six journeys are generated from two generativejourneys.

FIG. 22 depicts a clustering process.

FIG. 23 shows a solution that reduces the Jaccard distance.

FIG. 24 shows a drop in the average absolute distance in the number ofjourneys.

FIG. 25 shows results of the genetic algorithm.

FIG. 26 shows results of the genetic algorithm.

FIG. 27 shows results using different setting configurations.

FIG. 28 depicts an example for the handling of reviews for a journal.

FIG. 29 displays relations using a process tree.

FIG. 30 depicts map abstractor steps.

FIG. 31 depicts results of the process tree.

FIG. 32 shows a screen capture of the application when merging theactivities.

FIG. 33 shows journey maps at three levels of abstraction.

FIG. 34 shows example code diagram for a retail situation.

FIG. 35 shows an example genetic process diagram.

FIG. 36 shows an example application of genetic operations.

FIG. 37 shows an example method for adding noise to a sequence ofactivities.

FIG. 38 shows an example binary combination of complexity results ineight different datasets.

FIG. 39 shows an example summary of notation used herein.

FIG. 40 shows example operations for getting to the level of complexity.

DETAILED DESCRIPTION

Embodiments of methods, systems, and apparatus are described throughreference to the drawings.

FIG. 1 is a schematic diagram of platform 100 for clustering and datavisualizations according to some embodiments. The platform 100 canimplement aspects of the processes described herein for clustering datausing clustering engine 118 and generating data visualizations usingdata visualization tool 116.

The platform 100 generates data visualizations for interface application130 and implements event-based behavior analysis or trace analysis usingclustering engine 118. The clustering engine 118 implements clusteranalysis using code that processes input data to group a set of objectsin clusters based on detected patterns or similarities. Machine learninginvolves programming code stored in memory 108 that is configured tolearn, adapt or improve performance on a specific task with data,without being explicitly programmed using training engine 114 and neuralnetworks 112. Clustering engine 118 can implement machine learning withcluster analysis to learn patterns and similarities when processinginput data. Clustering engine 118 uses machine learning and clusteranalysis to generate output data, and integrates with data visualizationtool 116 to generate dynamic, interactive visual elements based on theoutput data for display at interface application 130. The interfaceapplication 130 can receive control commands that can be received asinput data and trigger generation of updated visual elements byclustering engine 118 and data visualization tool 116.

The platform 100 has a processor 104 and a memory 108 storing machineexecutable instructions to configure the processor 104 to receive inputdata (from e.g. entities 140, data sources 160). The platform 100connects to interface application 130, entities 140, network endpoints150, and data sources 160 (with databases 170) using network 140.Entities 140, interface application 130, and data sources 160 (withdatabases 170) can interact with the platform 100 to provide input dataand receive output data. For example the platform 100 can Network 140(or multiple networks) is capable of carrying data and can involve wiredconnections, wireless connections, or a combination thereof. Network 140may involve different network communication technologies, standards andprotocols, for example. The interface application 130 can be installedon user device to display an interface of visual elements, for example.

The platform 100 can include an I/O Unit 102, a processor 104,communication interface 106, and data storage 110. The processor 104 canexecute instructions in memory 108 to implement aspects of processesdescribed herein. The processor 104 can execute instructions in memory108 to configure neural networks 112, training engine 114, datavisualization tool 116, clustering engine 118, and other functionsdescribed herein. For example, training engine 114 can train neuralnetworks 112 using training data for cluster analysis. The clusteringengine 118 can use the trained neural networks 112 to generate outputdata for data visualization tool 116. The neural networks 112 cangenerate predictions for event traces or journeys, for example. Theclustering engine 118 may be software (e.g., code segments compiled intomachine code), hardware, embedded firmware, or a combination of softwareand hardware, according to various embodiments.

The data storage device 110 has a data warehouse model for storing eventtraces. An event trace has attributes that indicate activities overtime, for example. An event trace can be a set of touchpoints or asequence of activities. The data storage device 110 can store event logsthat represent a collection of event traces. The event traces can beused by platform 100 to generate journeys, that can be actual journeysor representative journeys. The event traces can be based on real-time(e.g. current) and/or historical data. The data visualization tool 116can generate visual elements for an interactive interface application130 by generating clusters for the event traces using the clusteringengine 118 to group event traces having similar attributes. Theclustering engine 118 can cluster similar traces that are close togetherusing distance measures. The clustering engine 118 can generate thehierarchical cluster by computing a distance measure for the attributesof the event traces to compute the similar attributes. The clusteringengine 118 can generate the hierarchical cluster by computing a distancemeasure based on a Levenshtein distance. The clustering engine 118 cangenerate the hierarchical cluster by computing a distance betweenclusters of the hierarchical cluster. The clustering engine 118 cangenerate a set of representative attributes for a set of event traces ofthe hierarchical cluster. The set of representative attributes computedbased on the similar attributes. Neural networks 112 can be used torepresent the event traces and the attributes or to determinerepresentative attributes based on patterns. Training engine 114 cantrain the neural networks 112 using event traces to identify patterns ofattributes or activities. In some embodiments, the pattern of activitiesindicates the set of representative attributes for a segment of thehierarchical cluster that corresponds to the set of event traces. Theclustering engine 118 can generate the hierarchical cluster based on aproximity measure using an order of activities for the event traces. Theclustering engine 118 can generate the hierarchical cluster based onlayers. A layer can correspond to a number of event traces that will begrouped based on the similar attributes, the layers corresponding to aheight of the hierarchical cluster. In some embodiments, the clusteringengine 118 generates the set of representative attributes based on thesimilar attributes and a frequent sequences mining process.

The data visualization tool 116 can generate the visual elements formultiple views for the interface application. The multiple views have afirst view indicating a pattern of activities for the set ofrepresentative attributes. A pattern entails many event traces andindicates representative attributes for the activities or activity itrepresents. The clustering engine 118 can use the neural network 112 todetect the patterns, for example. A second view can indicate thehierarchical cluster and the pattern of activities within thehierarchical cluster. The second view can highlight the location of thepattern of activities in the visual representation of the hierarchicalcluster. In some embodiments, the second view indicates the segment ofthe hierarchical cluster for the pattern of activities indicated in thefirst view. A third view indicates descriptors for the hierarchicalcluster. The third view corresponds to the level of granularity of thefirst and second view and the descriptors indicate why the subset of thecluster is interesting, for example. The descriptors can indicatedistinct traces, total traces and salient characteristics. In someembodiments, the processor computes salient characteristics for thedescriptors to indicate indexes based on a chi-square test.

The multiple views have selectable portions that trigger the datavisualization tool 116 to generate updates for the visual elements. Theplatform 100 controls rendering of the interface application 130 at adevice to display the multiple views synchronously. Responsive to aselection of a selectable portion of the plurality of selectableportions, the platform 100 controls rendering of the interfaceapplication 130 at the device to update the multiple views synchronouslybased on the selected portion to navigate the hierarchical cluster. Forexample, the selectable portions can select attributes (e.g. activities)to filter the views (e.g. event trace contains selected activity X). Insome embodiments, the selection portion is within a view of the multipleviews to trigger an update to the other views of the multiple views. Insome embodiments, the data visualization tool 116 updates the multipleviews synchronously based on the selected portion to navigate thehierarchical cluster at different levels of abstraction or granularity.

In some embodiments, the platform 100 receives a navigation goal at theinterface application 130. The goal indicates an attribute, for example.Responsive to the navigation goal, the platform 100 controls renderingof the interface application 130 at the device to update the multipleviews synchronously to indicate data based on the navigation goal.

In some embodiments, the interface application 130 can show a viewindicating actual event traces and a view showing representative eventtraces. The representative event traces being less than the actual eventtraces to provide an abstracted representation of the event traces. Itmay be difficult to see trends and insights from actual events traces ifthere is too much raw data shown at the interface application 130.

In some embodiments, clustering engine 118 can compute clusters of theevent traces based on a genetic process function. In some embodiments,for each event trace, the attributes are represented as atwo-dimensional tuple of activities over time and contextual data andthe genetic process function can evaluate the two-dimensional tuples ofthe event traces.

Each cluster can correspond to a set of event traces and arepresentative event trace based on representative attributes of the setof event traces. The genetic process function maps each event trace to acluster. The data visualization tool 116 can generate the visualelements for the interface application 130 and the visual elements canindicate the clusters and, for each cluster, the representative eventtrace based on the representative attributes. The representative eventtrace summarizes the set of event traces corresponding to the cluster.The data visualization tool 116 can control rendering of the interfaceapplication 130 at the device to display the visual elements and aplurality of selectable portions. Responsive to a selection of aselectable portion of the plurality of selectable portions, datavisualization tool 116 can control rendering of the interfaceapplication 130 to navigate the clusters. The clustering engine 118 candetermine segments for the event traces and then evaluate them todetermine the clusters, for example. The clustering engine 118 can groupthe event traces into X groups and then identify a representative eventtrace for each group. The representative event trace does not have to bean actual event trace or representative of actual raw data but cansummarize the group or cluster of event traces it represents. In someembodiments, the platform 100 can use neural networks 112 for naturallanguage processing of text data that can form part of event traces todetermine a sentiment score or sentiment indicator. This sentiment scoreor sentiment indicator can be an attribute for the event trace, forexample. The genetic approach goes through different combinations ofclusters and evaluates the clusters to determine the best segments ofevent traces.

In some embodiments, clustering engine 118 can implement the geneticprocess function by: evaluating initial set representative event tracesto generate elite set representative event traces, generating additionalinitial representative event traces using a transformation process,evaluating the additional initial representative event traces togenerate additional elite set representative event traces, continuingthe generating and the evaluating until stopping criterion is met, usingresulting elite representative event traces as the representative eventtraces for the clusters.

In some embodiments, clustering engine 118 computes, for each cluster,the representative event trace as a pattern of activities to summarizethe patterns contained within the set of event traces of the cluster. Insome embodiments, clustering engine 118 implements the genetic processfunction by pre-processing the event traces, determining an initialpopulation that corresponds to a number of the clusters, evaluatinginitial representative event traces, evaluating stop criteria,implementing a genetic operation to generate additional initialrepresentative event traces, outputting a map abstraction for use ingenerating additional visual elements. In some embodiments, clusteringengine 118 is configured to evaluate the initial representative eventtraces based on quality criteria for fitness, a number ofrepresentatives, a contextual distance and an average quality. In someembodiments, the clustering engine 118 is configured to weigh thequality criteria to generate an overall quality and average the overallquality. In some embodiments, the clustering engine 118 is configured toevaluate the initial representative event traces based on internal andexternal evaluation metrics.

In some embodiments, the data visualization tool 116 is configured togenerate visual elements for an interactive interface application by:generating a mapping of process mining elements to the event traces, themapping having an XML-based set of concepts. The mapping links processde facto models and de jure models to actual and expected customerexperiences; generating a data structure for hierarchical components ofa journey map, each component has an element and an attribute. Themapping can be stored in data storage 110. The journey map has at leastone element being a root node of the hierarchical components. The actualand expected customer experiences correspond to the journey map. Thedata visualization tool 116 is configured to generate visual elementsfor the components of the journey map and transmit the visual elementsto an interface application for display on a device;

In some embodiments, the data visualization tool 116 is configured togenerate visual elements for a map abstractor of an interactiveinterface application by: generating a process tree for the event tracesby grouping the event traces based on similar attributes, the processtree having nodes corresponding to the event traces, the process treehaving leaf nodes. The data visualization tool 116 can parse the processtree, and starting from leaf nodes of the parsed process tree,iteratively generates a prompt at the interface application 130 to mergea set of event traces that belong to a subset of the parsed processtree. Upon receiving confirmation to merge the set of event traces, datavisualization tool 116 can generate a name for the set of event tracesand replace the nodes corresponding to the set of event traces with anew node indicating the name and a set of representative attributes forthe set of event traces. This updated representation can be stored atdata storage 110. The data visualization tool 116 can generate thevisual elements for the interface application 130 that indicate theprocess tree and the new nodes for representing an abstracted processtree and controls rendering of the interface application 130 to displaythe visual elements. In some embodiments, a process tree is an abstracthierarchical representation of a process model, where the leaves areannotated with activities and all the other nodes are annotated withoperators. In some embodiments, a merge of activities for a first dataset can enable training engine 114 and neural network 112 to learn asimilar model with the same activities that can be applied the samemerge or abstraction rules.

The I/O unit 102 can enable the platform 100 to interconnect with one ormore input devices, such as a keyboard, mouse, camera, touch screen anda microphone, and/or with one or more output devices such as a displayscreen and a speaker.

The processor 104 can be, for example, any type of general-purposemicroprocessor or microcontroller, a digital signal processing (DSP)processor, an integrated circuit, a field programmable gate array(FPGA), a reconfigurable processor, or any combination thereof.

Memory 108 may include a suitable combination of any type of computermemory that is located either internally or externally such as, forexample, random-access memory (RAM), read-only memory (ROM), compactdisc read-only memory (CDROM), electro-optical memory, magneto-opticalmemory, erasable programmable read-only memory (EPROM), andelectrically-erasable programmable read-only memory (EEPROM),Ferroelectric RAM (FRAM) or the like. Data storage devices 110 caninclude memory 108, databases 112 (e.g. graph database), and persistentstorage 114.

The communication interface 106 can enable the platform 100 tocommunicate with other components, to exchange data with othercomponents, to access and connect to network resources, to serveapplications, and perform other computing applications by connecting toa network (or multiple networks) capable of carrying data including theInternet, Ethernet, plain old telephone service (POTS) line, publicswitch telephone network (PSTN), integrated services digital network(ISDN), digital subscriber line (DSL), coaxial cable, fiber optics,satellite, mobile, wireless (e.g. Wi-Fi, WiMAX), SS7 signaling network,fixed line, local area network, wide area network, and others, includingany combination of these.

The platform 100 can be operable to register and authenticate users(using a login, unique identifier, and password for example) prior toproviding access to applications, a local network, network resources,other networks and network security devices. The platform 100 canconnect to different machines or entities 140.

The data storage 110 may be configured to store information associatedwith or created by the platform 100. Storage 110 and/or persistentstorage 114 may be provided using various types of storage technologies,such as solid state drives, hard disk drives, flash memory, and may bestored in various formats, such as relational databases, non-relationaldatabases, flat files, spreadsheets, extended markup files, and so on.

The platform 100 can implement a processing mining based model for traceanalysis or event-based behavior analysis. The platform 100 can generatean appropriate mapping for process mining in order to help trace userdata and customer experience. The platform 100 can use XML-based set ofconcepts to implement the above mapping, for example. The platform 100can codify a parallel that exists between different types of businessprocess models (e.g. “de facto” and “the jure”) and the ways that existto analyze a customer experience (e.g. actual and expected).

Customer journey maps (journey maps) are data structures that model datarelating to customers' behavior. Platform 100 interacts with interfaceapplication 130 to display visual representations of journey mapsgenerated by data visualization tool 116 to assist in decision making.The platform 100 can use a journey map model with process mining, a dataanalytics technique to assess the impact of the journey's duration onthe customer experience.

journey map is a technique that enables professionals to betterunderstand customers' experiences when they interact with the stepsinvolved in a service. These interactions, called touchpoints, areincreasing and can be used by customers in erratic ways. Hence, beingable to use journey maps is becoming increasingly important forcompanies. journey maps can be applied to sales, healthcare, and libraryscience, for example.

FIG. 2 shows a journey map 200 depicting factors influencing vaccinationdecisions. The diagram indicates elements that can be found on journeymaps: touchpoints (e.g., consultation experience), stages (e.g.,pre-vaccination), and emotions (i.e., factors influencing the decision).

Two example journey maps include anticipated journey (e.g. expectedjourney) and actual journey, which aims to describe how the journey was“really” experienced by customers. There can be an interplay betweenexpected and actual journeys. For instance, traces of customer journeysavailable in platform 100 and/or data sources 160 can be used to build ajourney map from data or facts. Then, this journey map can be comparedwith an expected journey map generated by a prediction model.

Process mining relates to the use of process models and events logs todeliver fact-based insights. The platform 100 can use process mining forjourney maps. Process mining works with event logs, a sequential formatideal for representing journey maps. Working with expected and actualmodels is at the core of a process mining framework. An example relatesto process mining in the context of sales and how a structured approachcan facilitate a sales method. Embodiments can apply process to journeymap data.

Embodiments can define components of journey maps, develop a model forjourney maps, use process mining to analyze journey maps, and additionalanalytics-oriented usage of journey maps.

There are various components of a journey map such as customer, journey,mapping, goal, touchpoint, timeline, channel, stage, experience, lens,and multimedia, for example.

Customer. A customer is the stakeholder experiencing a service. This caninclude people such as patients, students, or software users. A customercan be defined by sociodemographic information. When a customer ismentioned as a fictional character, the term “persona” is sometimesused.

Journey. A journey map contains at least one journey, which is a paththrough a graph of nodes that relates to a path followed by a customer.An example journey map can relate to internal stakeholders to describewhat an ideal journey would look like, which identifies opportunitiesfor novel services or is employed as a diagnostic tool. This can bereferred to as an expected journey. In contrast, the actual journeyshowcases how a journey is experienced by the customer, finds existingcustomers' problems or needs or pictures the consumption of services bycustomers.

Mapping. Mapping is a process consisting of tracking and describingcustomers' responses and experiences when using a service. Ultimately,these elements are reported on a map.

Goal. A customer journey should be mapped with a goal in mind, which isalso referred to as scenario, prompts, story, or main intention. Ittriggers interactions with users, and streamlines the thought processfor users.

Touchpoint. A touchpoint is an interaction between customers andcompanies' products or services such as “searching for a product”, or“finding seats”. The arrangement of touchpoints can be cyclic: acustomer can iterate a few times over the same touchpoints. Moreover,the arrangement can be non-linear: (1) most of the time, the customerwill not go through all the existing touchpoints; (2) the customer mightmiss a planned touchpoint; and (3) the customer can unexpectedly quitthe journey.

Timeline. The timeline describes the duration of the journey from thefirst until the last touchpoint. Data can also not have a timestamp. Anumber attached to an event (i.e., touchpoint) can depict the sequencewithin the timeline.

Channel. The channel is the method chosen by the customer to interactwith the touchpoint such as a “reference desk” or “social media”.

Stage. A stage, encompasses several touchpoints. Some authors used thesplits: before, during, and after the experience, but employingdomain-related steps is also possible. For instance, the stage refers tothe waterfall model (i.e., software development). Some journey mapsmight not use stages.

Experience. The experience encompasses customers' feedback and emotions.There can be elements to express the experience. One element can be theemotion. Using only one continuum of emotions—such as unhappy tohappy—may fail to depict a customer's experience. Thus, describing theemotion can require some flexibility or a range of values. An elementcan be a scale that measures how positive or negative the experience wasfor the customer. Another element may be customers' quotes to representwhat customers have been through.

Lens. Some components of journey maps are domain-specific. For instance,there can be an appended layer to indicate the weather because itimpacts customer satisfaction when using the service. The layer canrefer to the term lens to reflect that multiple views are possible onthe same map. Suggestions and opportunities are some other examples oflenses superposed on top of touchpoints. They can promote reflection andanalysis of what happened during the journey. The lens can triggerdifferent visual representations on interface application 130 and datavisualization tool 116 can generate different lens for a journey mapinvolving different visual elements.

Multimedia. The usage of multimedia as part of a visual representationon interface application 130 can make a journey map engaging. Forinstance, recording customers while they are filling out the journey mapallows to better understand them. Multiple types of multimedia can beused such as audio, video, photos, and sketches.

FIG. 3 shows an example journey map model 300. The model 300 includes anexample XML data structure to store journey maps using concepts(journey, customer, touchpoint, experience, channel, stage, lens)arranged in a hierarchical structure. The structure can be used as animport log for process mining. The platform 100 can generate the journeymap model 300.

The example delineates a hypothetical situation in the retail industry.Alice (a customer) called to complain. Therefore, the top managementdecided to map her journey based on historical data. They observed threeevents: (1) Alice asked for a quotation, (2) she received the quotation,and (3) the call she made to complain. Example code may be illustratedin FIG. 34 .

In order to map the journey, the model 300 has a root element “journeymap”. The model 300 allows for the description of a “goal” describedwithin an element “string”. This notation refers to attribute coding.Going down the tree or hierarchical data structure, an element “journey”is a child node to the root element. This journey map model 300 containsonly one journey, but the platform 100 can allow for more than one(denoted by * in FIG. 3 ). The model 300 has the “customer” attribute,which can describe Alice. Different levels of detail are provided foreach touchpoint that illustrates the flexibility of the model 300. Forinstance, the first one indicates only the name of the touchpoint andthe time, while the second one has a lens “observation”. Finally, themodel 300 represents the “experience” using two attributes: emotion, andscale.

The model 300 can consider front-stage activities; i.e., the onesvisible to the customer. The model 300 can consider the path to bepotentially cyclic and non-linear (e.g. “Touchpoint.”, excludinggateways (e.g., “XOR”). Customers' paths can only be influenced, but notcontrolled. Moreover, a journey represents a path performed by a singlecustomer. If that was not the case (i.e., gateways allow a single pathto represent multiple alternative journeys), the multiplication oftouchpoints and the freedom that customers have to navigate through themin their preferred orders may lead to a meaningless map showing thatanything can happen in any order (referred to as a “flower model” inprocess mining). These decisions can reduce the complexity of the modelby emphasizing the main goal of journey maps.

Platform 100 can integrate journey map models with process mining andprovide a set of tools that support multiple ways to discover, monitor,and improve processes based on real event logs. Platform 100 can providea link between process models (e.g., BPMN) and the “reality” captured inevent logs. We distinguish a “de jure” model from a “de facto” model.The former is normative, as it intends to steer or control the reality.In contrast, de facto models aim to delineate reality. Process miningprovides a logic code to analyze or join both worlds. For instance, onecan discover a “de facto” model from the event logs. In turn, one cancompare this former model with a “de jure” one.

FIG. 4 shows the mapping 400 of the XES concepts to a model generated byplatform 100. Once the mapping 400 is done, the data can be analyzedwith process mining. For example, a company can use event logs to answerthe following question: does the duration between asking the quotationand receiving it affect the customer's experience?

FIG. 5 shows the process mining framework with a journey map modelextension 500 that can be stored by platform 100. The top portion showsthe process mining framework. The model can be implemented using XES forprocess mining to integrate journey map with the process miningframework. Platform 100 can combine data on top of models in processmining to provide a basis to tackle customer journeys using a rigorousapproach. Respectively, the expected and actual journey maps correspondto the “de jure” and “de facto” process models.

FIG. 6 shows the application of process mining activities 600, 602 onjourney map data and results 604. This can involve data pre-processing600 by platform 100 to generate synthetic journeys using journey mapmodels. The platform 100 can map the journeys to XES. This can involveprocess mining 602 by platform 100 to create a set of journeys thatresult in a neutral or positive experience and a set of journeys thatresult in a negative experience. This can involve hiding touchpoints.The platform 100 generates visualizations or visual elements of thejourneys for display at interface application 130.

The platform 100 can generate models that are easily exploitable by dataanalytics tools, is extensible to fit a domain-specific application, andit is not tool-dependent. By bringing process mining techniques andjourney maps closer together, platform 100 can close the gap betweenactual and expected journey maps and we shed light on a potential newarea of research, which requires further investigations with real-lifecollections of journey maps.

The platform 100 uses process mining that integrates with journey mapspecificities and cluster journeys and their representatives, to predictthe next customer's touchpoint, and to navigate among the journeys.

The platform 100 can use neural networks 112 for natural languageprocessing and sentiment analysis of text data from journey map data togenerate tools for process mining.

The platform 100 can model and used a dynamic Data Warehouse (e.g atdata storage 110) for storing the event traces in order to facilitatefast access and storage of historical (processed) data. The platform 100can implement a process for clustering the traces using hierarchicalclustering. An example trace can be a customer journey map (or journeymap). A journey map can indicate or represent different journeys. Theclustering can be based on the Levenshtein distance, for example. Thishas not been used with events that are characterized by severaltouch-points. The platform 100 can implement a process for selectingspecific attributes/touchpoints and guiding the analysis of focusedareas of the hierarchical clustering tree through the notion of “goals”.The platform 100 can generate visualizations composed of different viewsthat are updated synchronously to allow end-user to navigate withintraces. These views fulfill distinct objective: 1) The first one allowsto understand the pattern of activities, 2) the second one offers aholistic view of the hierarchical cluster, and 3) the last one is anarea to display descriptive statistics.

The platform 100 can configure interface application 130 to provide atrace interface that uses hierarchical clustering and indexes forinteractive navigation through numerous traces stored as event logformats, for example. The interactive navigation by interfaceapplication 130 enables exploration of the underlying traces of thewhole set of data available to platform 100 or driven by user goals inorder to examine events and patterns in specific areas of interest.

Traces can provide an understanding of the quality of customerexperience at an end-to-end level. The ever growing amount of servicesoffered to users for consumption has made the ability to understandtheir behavior very important. Similarly important is the knowledgeextracted by the increasing number of ways organizations interact withtheir customers; e.g., a customer might visit a physical store, purchasea product online, and provide feedback on social media which can becollected as a holistic data set for use by platform 100. Traces allowfor better understanding of a customer's end-to-end experience whenusing a service by mapping any interactions with the company (calledtouchpoints) on a map that ultimately contributes to betterunderstanding and serving customer needs.

A model for traces can be integrated with process mining techniques. Theplatform 100 can configure interface application 130 to explore numerouscustomer journeys at the same time. The platform 100 can take event logsas input, without using any a-priori information and generate visualelements for journeys. Visualizing event logs on traces enables focus onpersonal customer activities (e.g., by incorporating customer emotions),rather than the “internally-focus problem-solving approach” and tracescan incorporate customer journeys that are deemed exceptional behaviors,rather than removing them to increase model readability.

Representing many customer journeys in an intelligible can be achallenge. There are limits on the number of journeys to be compared(e.g. less than ten), making the overall process relativelystraightforward. However, we argue that companies in the serviceindustries tend to deal with hundreds or thousands of journeys. Toovercome this challenge and identify different areas of interest, ahierarchical clustering process is employed to segment the originaldata. The hierarchical nature allows for a top-down navigation ofautomatically generated groups of similar journeys. Once the clustersare formed, platform 100 and interface application 130 are able toleverage the contextual information that comes along a typical customerjourney such as the customers' characteristics, or the emotions. Theplatform 100 can use indexes in order to explain why the differentclusters were generated. The platform 100 can let the users define theirown exploration goals, making it the first goal-oriented tool thatallows analysts to set a-priori goals to guide their journeyexploration.

The platform 100 can configure interface application 130 with visualrepresentations to show how numerous event logs can be displayed ontotraces and can let users navigate into these journeys.

The platform 100 can let users upload and explore their own datasetusing a trace or journey map layout. To limit the number of journeysdisplayed on the interface application 130 and allow for an intuitiveexploration, the platform 100 uses hierarchical clustering.

FIG. 7 shows an example hierarchical clustering tree 700 that can begenerated by the platform 100. Each letter represents an activity of anevent trace and the tree is built bottom up by merging the activitiesthat are most similar at each iteration. By default, the platform 100uses a hierarchical process based on a proximity measure that takes intoaccount the order of activities and is a variant of the Jaccardsimilarity based on shingles. In the clustering tree 700 (ordendrogram), the journeys seen in the event logs are at the leaf leveland as they get merged they form “representative” journeys or eventtraces. That is, a journey can refer to an event trace. A representativejourney is a single pattern of activities whose purpose is to summarizethe patterns contained in a cluster. Because the representative journeysat the top of the tree summarize many—potentially distant—journeys, itwill tend to show only few activities shared by many. In contrast, therepresentative journeys closer to the leafs will show more details. Thefirst layers show general patterns and hide less importantactivities—like a world map would omit small cities. However, interfaceapplication 130 allows a drill-down into ‘countries’ of interest (i.e.,pattern of activities), which would redirect to new traces where thepreviously hidden ‘cities’ (i.e., omitted activities) will be shown oninterface application 130.

FIG. 8 is an example screenshot visible at interface application 130 andpoints out three views: journey map view 802, tree view 804, and textualview 806 that are available to navigate the clusters. Each of theseviews fulfills specific objectives. First, the journey map view 802shows journeys that are in the same cluster. This representation allowsto easily compare the pattern of activities. Second, the tree view 804displays the hierarchical structure of the journey clusters—useful inproviding a holistic view of the clusters and current location. Third, abox per cluster view 806 provides a convenient means to display indexesnamed “salient characteristics”. In this example, the salientcharacteristics is the top 5 results of a chi-square test applied on allthe contextual information. For instance, if at a global level (theentire dataset) the number of women is equal to the number of men, itmight be surprising to find a cluster with large majority of women.Therefore, this information might come up as one of the top 5 salientcharacteristics.

Moreover, the user might be interested in specific characteristicsoccurring during the journey. For this reason, we allow user-definedgoals. For instance, one might be interested in journeys that started bythe activity “attending class” experienced by young people.

The data storage device 110 has a data warehouse model for storing eventtraces. An event trace has attributes that indicate activities overtime, for example. An event trace can be a set of touchpoints or asequence of activities. The data storage device 110 can store event logsthat represent a collection of event traces. The event traces can beused by platform 100 to generate journeys, that can be actual journeysor representative journeys. The event traces can be based on real-time(e.g. current) and/or historical data.

The data visualization tool 116 can generate visual elements of aninterface 800 for an interactive interface application 130 by generatingclusters for the event traces using the clustering engine 118 to groupevent traces having similar attributes. The clustering engine 118 cancluster similar traces that are close together using distance measures.The clustering engine 118 can generate the hierarchical cluster bycomputing a distance measure for the attributes of the event traces tocompute the similar attributes. The clustering engine 118 can generatethe hierarchical cluster by computing a distance measure based on aLevenshtein distance. The clustering engine 118 can generate thehierarchical cluster by computing a distance between clusters of thehierarchical cluster. The clustering engine 118 can generate a set ofrepresentative attributes for a set of event traces of the hierarchicalcluster. The set of representative attributes computed based on thesimilar attributes. In some embodiments, the pattern of activitiesindicates the set of representative attributes for a segment of thehierarchical cluster that corresponds to the set of event traces. Theclustering engine 118 can generate the hierarchical cluster based on aproximity measure using an order of activities for the event traces.

The data visualization tool 116 can generate the visual elements formultiple views 802, 804, 806 for the interface 800. A first view 802indicates a pattern of activities for the set of representativeattributes. The first view 802 can indicate representative event tracesas an ordered set of activities (home, recreation, shopping, work) overtime. A pattern or representative event trace entails many event tracesand indicates representative attributes for the activities itrepresents.

A second view 804 can indicate the hierarchical cluster and the patternof activities within the hierarchical cluster. The second view canhighlight the location of the pattern of activities in the visualrepresentation of the hierarchical cluster. In some embodiments, thesecond view indicates the segment of the hierarchical cluster for thepattern of activities indicated in the first view. The clustering engine118 can generate the hierarchical cluster based on layers and a subsetof the layers can be shown in the view 804. A layer can correspond to anumber of event traces that will be grouped based on the similarattributes. The layers correspond to a height of the hierarchicalcluster. In some embodiments, the clustering engine 118 generates theset of representative attributes based on the similar attributes and afrequent sequences mining process.

A third view 806 indicates descriptors for the hierarchical cluster. Thethird view 806 corresponds to the level of granularity of the first andsecond view 802, 804 and the descriptors indicate why the subset of thecluster is interesting, for example. The descriptors can indicatedistinct traces, total traces and salient characteristics. In someembodiments, the processor computes salient characteristics for thedescriptors to indicate indexes based on a chi-square test.

The multiple views 802, 804, 806 have selectable portions that triggerthe data visualization tool 116 to generate updates for the visualelements. The platform 100 controls rendering of the interfaceapplication 130 to display the multiple views 802, 804, 806synchronously. Responsive to a selection of a selectable portion of theplurality of selectable portions, the platform 100 controls rendering ofthe interface application 130 to update the multiple views 802, 804, 806synchronously based on the selected portion to navigate the hierarchicalcluster. For example, the selectable portions can select attributes(e.g. activities) to filter the views 802, 804, 806 (e.g. event tracecontains selected activity X). In some embodiments, the selectionportion is within a view of the multiple views to trigger an update tothe other views of the multiple views. For example, a selection in view804 can trigger an update to the other views 802, 804. In someembodiments, the data visualization tool 116 updates the multiple viewssynchronously based on the selected portion to navigate the hierarchicalcluster at different levels of abstraction or granularity.

In some embodiments, the interface application 130 can show a viewindicating actual event traces and a view showing representative eventtraces. The representative event traces being less than the actual eventtraces to provide an abstracted representation of the event traces. Itmay be difficult to see trends and insights from actual events traces ifthere is too much raw data shown at the interface application 130.

In some embodiments, the platform 100 receives a navigation goal at theinterface application 130. The goal indicates an attribute, for example.Responsive to the navigation goal, the platform 100 controls renderingof the interface application 130 at the device to update the multipleviews synchronously to indicate data based on the navigation goal.

FIG. 9 shows a screenshot 900 of interface application 130 for goalsetting impact on a tree. The top part displays the settings, while thebottom part shows that some branches of the tree are interesting withregards to the goal (red “hot” area at the top). Hence, the application130 allows navigation without using any a-priori information, but alsosetting navigation goals, and guidance by the resulting colors.

Finally, when moving from one view to the others, the three views areupdated on interface application 130 synchronously, allowing a smoothexploration amongst journeys.

The platform 100 can involve main elements: 1) a web interface; 2) theXES-parser; 3) Hcluster; and 4) a data warehouse. The elements includeparameters.

Web interface. For example, the web interface leverages bootstrap,jquery and d3js to provide a user-friendly interface to upload andnavigate journeys. Both the journey map view and the tree view areimplemented in d3js. There may be other implementations.

XES Parser. The platform 100 works with event logs. More specifically,platform 100 can leverage XES (eXtensible Event Stream) within processmining. The XES Parser can be a Java implementation that encapsulatesthe OpenXES library to parse XES file.

Hcluster. Hcluster is an implementation containing the three steps forexample. FIG. 10 illustrates example steps 1000. A step is thehierarchical clustering implemented using Scipy. Two parameters can beprovided as inputs: the distance measure between event sequences and themethods for calculating the distance between clusters. They can both bechosen by the user when uploading a dataset. FIG. 11 illustrates apreview of the parameters 1010 when uploading a dataset. It seems thatusing shingles as a distance metric provides a more intuitive way tonavigate through journeys. Once the hierarchical cluster is formed, thenext step consists of cutting the clustering to form layers. A layer isa set of predetermined number of journeys that will be grouped togetherand will ultimately appear on the same trace. To achieve this, theplatform 100 can recursively cuts the dendrogram returned by Scipy. Asmall number of journeys will lead to a simple journey map that is easyto visualize, but also works for more complex tree structures (i.e.,trees with larger height). Another step consists of finding therepresentative journey using a frequent sequences mining process.

Data Warehouse. Each dataset is saved by data storage 110 in its owndatabase schema designed as a star schema. Data storage can store a fullschema or all the information required to use the application (e.g.,clusters, journeys, events) as well as some precomputations. Forinstance, the platform 100 can count the number of occurrences for eachcharacteristic at each cluster, so the goals and the salientcharacteristics can be retrieved quickly.

Altogether, the parameters visible in FIG. 11 allow users to explore theexact same dataset from different perspectives.

The platform 100 can process many journeys for display onto journey mapor traces in an intelligible and efficient manner. The platform 100integrates process mining taskforce (i.e., XES) and process miningactivity (i.e., discovery).

The platform 100 can cluster events (traces) for the discovery of thenumber of segments (clusters) that exist. The platform 100 can implementa process that automatically finds the best representatives. Thistechnique is based on genetic processes. Except for the timing and thetitle of each activity in a trace of events, the platform 100 can takeinto account any contextual information of the activities, for examplelooks into any text the users wrote or any emotion they showed.

In some embodiments, clustering engine 118 can compute clusters of theevent traces based on a genetic process function. In some embodiments,for each event trace, the attributes are represented as atwo-dimensional tuple of activities over time and contextual data andthe genetic process function can evaluate the two-dimensional tuples ofthe event traces.

Each cluster can correspond to a set of event traces and arepresentative event trace based on representative attributes of the setof event traces. The genetic process function maps each event trace to acluster. The data visualization tool 116 can generate the visualelements for the interface application 130 and the visual elements canindicate the clusters and, for each cluster, the representative eventtrace based on the representative attributes. The representative eventtrace summarizes the set of event traces corresponding to the cluster.The data visualization tool 116 can control rendering of the interfaceapplication 130 at the device to display the visual elements and aplurality of selectable portions. Responsive to a selection of aselectable portion of the plurality of selectable portions, datavisualization tool 116 can control rendering of the interfaceapplication 130 to navigate the clusters. The clustering engine 118 candetermine segments for the event traces and then evaluate them todetermine the clusters, for example. The clustering engine 118 can groupthe event traces into X groups and then identify a representative eventtrace for each group. The representative event trace does not have to bean actual event trace or representative of actual raw data but cansummarize the group or cluster of event traces it represents. In someembodiments, the platform 100 can use neural networks 112 for naturallanguage processing of text data that can form part of event traces todetermine a sentiment score or sentiment indicator. This sentiment scoreor sentiment indicator can be an attribute for the event trace, forexample. The genetic approach goes through different combinations ofclusters and evaluates the clusters to determine the best segments ofevent traces.

In some embodiments, clustering engine 118 can implement the geneticprocess function by: evaluating initial set representative event tracesto generate elite set representative event traces, generating additionalinitial representative event traces using a transformation process,evaluating the additional initial representative event traces togenerate additional elite set representative event traces, continuingthe generating and the evaluating until stopping criterion is met, usingresulting elite representative event traces as the representative eventtraces for the clusters.

In some embodiments, clustering engine 118 computes, for each cluster,the representative event trace as a pattern of activities to summarizethe patterns contained within the set of event traces of the cluster. Insome embodiments, clustering engine 118 implements the genetic processfunction by pre-processing the event traces, determining an initialpopulation that corresponds to a number of the clusters, evaluatinginitial representative event traces, evaluating stop criteria,implementing a genetic operation to generate additional initialrepresentative event traces, outputting a map abstraction for use ingenerating additional visual elements. In some embodiments, clusteringengine 118 is configured to evaluate the initial representative eventtraces based on quality criteria for fitness, a number ofrepresentatives, a contextual distance and an average quality. In someembodiments, the clustering engine 118 is configured to weigh thequality criteria to generate an overall quality and average the overallquality. In some embodiments, the clustering engine 118 is configured toevaluate the initial representative event traces based on internal andexternal evaluation metrics.

Summarizing numerous customer trajectories is challenging. The platform100 uses clustering to summarize sequences of categorical data. Usingsynthetic datasets simulating customer journeys, as well as a realdataset, the platform 100 is flexible and returns customer journeys ofbetter quality.

A customer experience can be defined as a customer's journey with a firmover time across multiple interactions called touchpoints. A journey canbe as simple as a single activity (e.g., ‘looking at a product’), butcan also involve complex interactions through various channels, multipledevices, and from several locations. Customer interactions areincreasing, services are becoming more complex, and customers can beunpredictable. Moreover, there is an increasing number ofcustomer-to-customer interactions, which are drivers of variability andunpredictability. Overall, offering a great customer experience is achallenging task, partly due to the lack of conceptual clarity.Furthermore, a lack of a unified understanding of customers can be anobstacle to mobilizing employees around integrated touchpoints,journeys, and consistent experiences.

Concretely, a challenge faced by many practitioners is to make sense ofthe—potentially infinite—combination of activities that exist in orderto consume a service. A trace is a conceptual tool used to betterunderstand customers' trajectories when they are consuming a service. Itdepicts typical journeys that will be experienced by the customersacross several touchpoints. Multiple types of journeys can be includedon a trace. When a trace is used as a design thinking tool by internalstakeholders to discuss, challenge, or innovate the way a service isoffered to customers, expected journeys are used. This implies a dyadicrelation in which it cannot be assumed that the experience is perceivedby customers as intended by the internal stakeholders. A trace can alsobe used to understand how the service is really perceived andexperienced by customers, either by directly eliciting their opinionthrough surveys and special tools, or by leveraging evidences stemmingfrom data. In this case, it can be referred to it as the actual journey.

The platform 100 can transform a set of actual journeys into a fewrepresentative journeys with the aim of displaying them on a interfaceapplication 130 as visual elements. FIG. 12 is an example screenshot1200 of interface application 130. This task is illustrated in the fourparts of FIG. 12 .

For the sake of the example, the screenshot 1200 defines five typicaltouchpoints 1202 with which a customer might interact when buying aproduct Then, screenshot 1200 can depict five journeys by event logs1204 composed of three to four touchpoints. We consider these journeysto be actual because they represent what the customers have reallyexperienced. Typically, the data can be extracted from software datasources 160. Moreover, the data can be formatted according to the XESformat for process mining. The important point is to be able to groupthe touchpoints from the same journey together (using a journeyidentifier). XES format can store customer journeys. Next, thescreenshot 1200 can display the five actual journeys 1206 from the eventlogs on a journey map or trace. Although five journeys can still beconsidered as a limited number of journeys that can display behaviors.This can reduce the complexity and looks at these journeys at a higherlevel of abstraction. Specifically, a representative journey 1208 is asingle pattern of activities whose purpose is to summarize similaractual journeys, which is a technique useful for characterizing typicaltrajectories. This shows how the usage of two representative journeys1208 helps in reducing a journey map's or trace's complexity. As it canbe seen, each actual journey is assigned to its closest representativejourney, e.g., the darker representative journey summarizes the actualjourneys 2 and 5. In this regard, finding representative journeys can beconsidered as a clustering task by platform 100.

Finding representative journeys involves platform 100 choosing the rightnumber of journeys and finding a sequence of activities that summarizesthe actual journeys well. Moreover, the results can depend on theunderlying motivation for building the journey map in the first place.For instance, if the goal is to have a general overview of the actualjourneys, there may be a different journey map than if the goal were toinvestigate peculiar arrangements of touchpoints. Finally, one may wishto find groups of similar customers solely based on their trajectories(i.e., the sequence of touchpoints), or based on their characteristics(e.g., age and region), or most probably, on a continuum between thesetwo extremes.

Given the challenges surrounding the desire to increase theunderstanding of customer behavior, the platform 100 can be configuredfor clarifying the customer journey discovery activity, proposing groundtruth datasets, which are particularly suited for evaluating thisactivity, and introducing a process to discover representative journeys.Using the proposed datasets and existing cluster analysis techniques,platform 100 can generate traces automatically in a flexible natureusing a real dataset and illustrate the results at interface application130.

The customer journey discovery activity can be described with thefollowing definition: given a set of actual journeys, find a reasonableamount of representative journeys that well summarizes the data. FIG. 39provides a summary of notation.

A touchpoint is an interaction between a customer and a company'sproducts or services. ‘Sharing on social network’ or ‘ordering a producton the website’—two activities visible in FIG. 12 —are two typicalexamples of touchpoints in a retail context. ‘Visiting the doctor’, or‘taking medication’ are other possible examples in a healthcare context.Let t be a touchpoint, and let T be the finite set of all touchpoints.

Ordered Set of Touchpoints involves one or more touchpoints. Whenconsuming a service, a customer will have a trajectory composed of oneor more touchpoints. Let S=t₁, t₂, . . . ; t∈T be a set of orderedtouchpoints. For instance, at 1204 the first journey starts with‘visiting the shop’, followed by ‘testing the product’, and finally‘sharing on social network’.

Contextual data is the data that holds contextual information relevantduring the service delivery. Typically, customers' demographics or thelevel of satisfaction of the customers are examples of contextual data.For instance, we might discover that certain trajectories are morepopular amongst younger customers. In this case, it would constituteimportant information for the marketing team in order to betteranticipate and communicate with this segment of customers. The intuitionis that discovering trajectories that can be described by particularcontextual data (e.g., the trajectories of younger customers from the‘region A’) provide more insights than finding representatives that areuniformly distributed amongst all customers' profile. Hence, adding thecontextual data in the journey allows to consider it when trying to findthe best representative journey. Let C be a set of all availablecontextual data. Let the name of the contextual data be c∈C and let #cbe its nominal value.

An actual journey J_(a) can be a tuple (S, C); i.e., an actual journeyis a nonempty and ordered set of touchpoints and a set of contextualdata. For instance, J_(a)={

‘Visiting the shop’, ‘Testing the product’, ‘Sharing on social network’

, {subscription-type:standard}, {age-range:young}} is a journey withthree touchpoints from a young customer having a standard subscription.

_(JA) can refer to an event log, the set of all actual journeys observedfrom customers.

A representative journey summarizes a subset of _(JA). Let this subsetbe _(JA). Let a representative journey J_(r) be a triplet (S, C,_(JA)⊆_(JA)); i.e., a representative journey is a nonempty and orderedset of touchpoints, and a set of contextual data, which summarizes asubset of _(JA). At 1208 there is shown two representative journeyssummarizing five actual journeys. As can be seen, the lighterrepresentative journey

BDAE

represents the actual journeys

BDA

,

BDAE

, and

BDE

.

A customer journey map is a conceptual tool used to provide an overviewof the customer experience through the use of representative journeys.It contains one or several representative journeys. Let a customerjourney map JR be the superset of all the J_(r). Let k_(R) denotes thenumber of journeys on a map (i.e., |J_(R)|).

Finally, for readability purposes, let σ_(A)={S: S∈J_(a), J_(a)∈J_(A)}and {S: S∈J_(r), J_(r)∈J_(R)}; i.e., σ_(A) is the set of all thesequences of activities observed from customers, while σ_(R) is the setof all the sequences of activities displayed on a customer journey map.Similarly for contextual data, let _(K) _(A) ={C: C∈J_(a), J_(a)∈_(JA)}and _(K) _(R) ={C: C∈J_(r), J_(r)∈_(JR)).

The customer journey discovery activity can be defined as a functionthat maps all members of _(JA) to a member of _(JR); i.e., that maps allthe actual journeys to representative journeys ultimately displayed on ajourney map. Note a journey J_(a) is assigned to a single representativejourney in _(JR).

Discovering _(JR) from _(JA) is an unsupervised clustering taskimplemented by clustering engine. The platform 100 determines aparameter for the number of k_(R). When the goal is to have a generaloverview about _(JA), it seems reasonable to have k_(R) in a range fromtwo to six journeys so the journey map is readable but this can vary.However, discovering few dozens J_(r) might also be a relevant choice ifthe goal is to catch complex and less generic patterns. Once k_(R) hasbeen found, the another interesting challenge lies in the fact thatJ_(a) is two-dimensional. Indeed, both S and C can be taken inconsideration when clustering the journeys. Finally, the sequence S∈Jrthat best summarizes its assigned actual journeys needs to be found. Itmight be the case that an ideal representative journeys was neverobserved but well summarizes the actual journeys.

The platform 100 uses a hierarchical structure so that the first layersshow only the most important activities, abstracting from less importantones. The platform 100 can explore without any a priori information, butit is also possible to set goals based on journey characteristics (e.g.,age and gender). Once the goals are set, areas of interest (i.e., theones fulfilling the goal) are highlighted at interface application 130.In platform 100, the dendrogram is built based on the edit distancebetween journeys. The platform 100 can also leverage the contextualinformation when finding representatives.

FIG. 13 is a diagram 1300 that shows that the process mining frameworkas well as the input data are relevant in a customer journey analyticscontext. Similar to the process discovery activity in process mining,platform 100 focuses on the discovery of a model from event logs. FIG.14 shows that these business process model (BPM) 1402 and journey maps1404 convey different information. A journey map depicts journeys asexperienced by customers while a BPM shows the available combination ofactivities using advanced constructs such as XOR or parallel gateways.Finally, the information that will be leveraged for a journey mapdiffers from that used for a BPM. For instance, the satisfaction, theemotion, and various customer characteristics are central pieces ofinformation that should be leveraged when discovering a journey map.Such information might sporadically be used to enhance a BPM, butusually only once the model has been discovered.

The platform 100 can summarize a set of events using representativesequences summarizing a set of sequences. There are different ways toselect a representative. The ‘frequency’, where the most frequent eventis used as the representative. The ‘neighborhood density’, which consistof counting the number of sequences within the neighborhood of eachcandidate sequence. The most representative is the one having thelargest number of sequences in a defined neighborhood diameter. The‘mean state frequency’: the transversal frequencies of the successivestates is used to find a representative using the following equation:

$\begin{matrix}{{{MS{F(s)}} = {\frac{1}{\ell}\sum_{i = 1}^{\ell}}}{fsi}} & (1)\end{matrix}$

where

s=s₁ s₂ . . . s_(I): Sequence of length l

f_(s1), f_(s2), . . . f_(sI): Frequencies of the states at time t_(l)

The sum of the state frequencies divided by the sequence length becomesthe mean state frequency. Its values is bounded by 0 and 1 where 1describes a situation where there is only one single distinct sequence[9]. The sequence with the highest score is the representative. The‘centrality’: the representative—or medoid—can be found using thecentrality. Being the most central object, the representative is thesequence with the minimal sum of distances to all other sequences.Finally, the ‘sequence likelihood’: the sequence likelihood of asequence derived from the first-order Markov model can also be used todetermine the representative.

The platform 100 can use genetic approach that uses contextual data andcan be an extension to the summarization of categorical sequences.

The platform 100 can use genetic approaches to discover business processmodels from event logs. The platform 100 tailors it towards journey mapby introducing specific evaluation metrics suited to measure the qualityof a journey map and its representative journeys given a set of actualjourneys. FIG. 15 depicts the main phases 1500: (1) a preprocessingphase, (2) generating the initial population, (3) assigning each actualjourney to its closest representative, (4) evaluating the quality of thejourney maps, (5) the stopping criterion evaluation, and (6) creatingnew journey maps by applying some genetic operations.

As an introduction, FIG. 16 provides some intuitions on the geneticprocess. Typically, a set of actual journeys, σ_(A), is provided to thealgorithm. First, during Generation 1 1602, the platform 100 can build pnumber of J_(R), where p is the population size; i.e., the number ofJ_(R)s that will be evaluated after each generation. In our example p=3.Each J_(R) is evaluated and we keep the e best J_(R)s, called the eliteset while we discard the other (p−e) J_(R)s. Then platform 100 can moveto generation 2, 1604 where it can keep an untouched version of the enumber of J_(R)s in the elite set. In FIG. 16 , if e=1, platform 100 cankeep the best J_(R) from generation 1, i.e., J_(R2) is intact ingeneration 2. The platform 100 can then apply some transformation togenerate (p−e) new J_(R)s that will, in turn, be evaluated. The platform100 can generate J_(R4) and J_(R5). The platform 100 can recursivelytransform and evaluate the p number of J_(R)s until a stopping criterionis met. Once a stopping criterion is met, we return the best J_(R)(J_(R7) in FIG. 16 at generation 3 1606). The best J_(R) can beinterpreted as the best set of representative journeys, J_(r),representing a set of journeys from J_(A) that have been found givencertain evaluation criterion. The platform 100 can generate the initialpopulation, apply various types of operations on each J_(R) to transformthem, and evaluate each one of them given a set of journeys in J_(A).

The platform 100 can make the assumption that σ_(R) will be close to thefrequent patterns observed in σ_(A).

Let σ_(Al) be the set of all actual journeys of length l and letTop_(l)={Top_(l)∈σ_(Al), |Top_(l)|=min(|σ_(Al)|, in)} be the i_(n) mostoccurring pattern of length l. Finally, let Top={Top⊇Top[i_(s),i_(e)]},i.e., Top is the superset of all the most occurring patterns of lengthsi_(s) to i_(e). FIG. 17 depicts Top 1700 from the running example givenin FIG. 16 .

Top is used later to form the initial population of _(JR), and to add arandom journey to _(JR). Using Top the platform 100 can avoid generatingjourneys by picking a random number of touchpoints from T. Using Top canreduce the execution time by two to get an output _(JR) of the sameaverage quality. For an example, platform 100 can fix i_(n) to 10, i_(s)to 2, and i_(e) to 12.

To build the initial population platform 100 can generate p number of_(JR) on which we add one sequence randomly picked from Top (defined inSect. 4.1). This is visible in the genetic process shown in FIG. 35 ,lines 2 to 5. As can be seen in FIG. 16 ,k_(R)=|J_(R1)|=|J_(R2)|=|J_(R3)|=1 because platform 100 can only add onesequence per _(JR).

To evaluate how good _(JR) describes J_(A), platform 100 can decomposethe problem. Indeed, the quality of _(JR) can be based solely upon itsrepresentative journeys. And, the quality of a representative journeycan be measured when knowing which actual journeys it represents. Hence,a first step toward evaluating the quality of _(JR) is to assign eachjourney J_(a)∈J_(A) to its closest journey in J_(r)∈_(JR). This isillustrated in FIG. 12 at 1208 where each journey J_(a) is assigned to ajourney J_(r).

To characterize the closeness between J_(a) and J_(r), platform 100 canuse the Levensthein distance. It is a metric to measure the distancebetween sequences. The Levensthein distance counts the number of editoperations that are necessary to transform one sequence into anotherone. There are three types of operations: deletions, insertions, andreversals (or substitutions). For instance, the distance between

ABC

and

ACCE

is 2 since one substitution and one insertion are required to matchthem.

The platform 100 can define the closest representative as the one havingthe smallest Levensthein distance with the actual journeys. Note that ifa tie occurs between multiple best representatives, platform 100 canassign the J_(a) to the J_(r) having the smallest amount of actualjourneys already assign to it. Moreover, when k_(R)=1 all the J_(a) willbe assigned to the same J_(r). This be the case after creating theinitial population because only one J_(r) is added to J_(R).

Once each actual journey has been assigned to its closestrepresentative, platform 100 can evaluate _(JR) using the criteriadescribed in the next section.

The platform 100 can use different evaluation criteria to determine thequality of each _(JR), namely, (1) the fitness, (2) the number ofrepresentatives, (3) the contextual distance, and (4) the averagequality. Using the example in FIG. 16 , FIG. 18 illustrates theevolution 1800 of the evaluation criteria for the best J_(R).

The fitness measures the distance between each sequence of activitiesσ_(A) and its closest representative σ_(R) using the Levenshteindistance.

$\begin{matrix}{{{Fitness}\;\left( {\sigma_{\mathcal{A}},\sigma_{\mathcal{R}}} \right)} = {1 - \frac{\sum\limits_{i = 1}^{\sigma_{\mathcal{A}}}{\min_{j = 1}^{\sigma_{\mathcal{R}}}\left( {{Levensthein}\left( {\sigma_{\mathcal{A}_{i}};\sigma_{\mathcal{R}_{j}}} \right)} \right)}}{\sum\limits_{i = 1}^{\sigma_{\mathcal{A}}}{{Length}\left( \sigma_{\mathcal{A}_{i}} \right)}}}} & (2)\end{matrix}$

-   -   where    -   σ_(R) _(i) : i^(th) actual sequence observed in event logs    -   σ_(R) _(j) : j^(th) representative contained in J_(R)    -   Levensthein(x₁,x₂); Levensthein distance between two sequences    -   Length(x): Length of the sequence of activity x

A fitness of 1 means that the representative journey perfectly catchesthe behavior of the actual journeys assigned to it. In contrast, afitness close to 0 implies that many edit operations are necessary tomatch the sequences.

Another evaluation criteria is the number of representatives. An eventlog can contain several hundreds or thousands of unique actual journeys.Hence, if platform 100 maximizes the fitness without trying to keep alow k_(R), the journey map can become unreadable because too manyrepresentative journeys may be displayed on it. In other words, J_(R)overfits. Hence, the goal is to find a k_(R) that offers a goodcompromise between underfitting and overfitting. Finding the optimalnumber of clusters is a recurrent challenge when clustering data. Theplatform 100 can involve integrating different ways of determining theideal number of clusters, such as the Bayesian information criterion,the Calinski-Harabasz index, or the Silhouette technique. The idea is toevaluate a range of solutions (e.g., from 2 to 10 journeys) and to keepthe best solution. Let k_(h) be the ideal number of clusters returned byone of the three techniques mentioned above. By integrating k_(h) intothe evaluation, platform 100 can guide the solution toward a k_(R) thatis statistically relevant. To evaluate the quality, platform 100 measurethe distance between k_(R) and k_(h). To do this, platform 100 can usethe following example distribution function:

$\begin{matrix}{{{NumberOfRepresentatives}\;\left( {k_{\mathcal{R}},k_{h},x_{0}} \right)} = \frac{1}{1 + \left( \frac{{k_{\mathcal{R}} - k_{h}}}{x_{0}} \right)^{2}}} & (3)\end{matrix}$

-   -   where    -   k_(R): Number of J_(r) journeys on J_(R) (i.e., |J_(R)|)    -   k_(h): Optimal number of journeys (e.g., using the        Calinski-Harabasz index)    -   x₀: x value of the midpoint

FIG. 19 illustrates a graph 1900 of the distribution for kh=6 and x₀=5.For instance, if k_(R)=2, it would have a quality of ≈0.6. Also notethat the parameter x₀ determines where the midpoint of the curve is.Concretely, k_(R)=1 or k_(R)=11 can result in a quality of 0.5 becausethe absolute distance from k_(h) is 5. The platform 100 set x₀=5 forexperiments.

The contextual distance allows platform 100 to consider the set ofcontextual data C when grouping similar journeys. The more distant theset of contextual data is between J_(a) that are represented by distinctJ_(r), the better the quality is. To measure the distance, platform 100can first build a value frequency table which count all the values perrepresentatives (see FIG. 20 ). Then, for each pair of clusters,platform 100 can calculate the cosine similarity, which is defined as:

$\begin{matrix}{{{ContextualDistance}\;\left( {v_{1},v_{2}} \right)} = \frac{v_{1} \cdot v_{2}}{{v_{1}} \cdot {v_{2}}}} & (4)\end{matrix}$

-   -   where        -   v₁: Value frequency counter for J_(r1)        -   v₂: Value frequency counter for J_(r2)

Finally, the cosine distances are average to get the overall contextualdistance. A short overall distance indicates that the contextual data ofJa that are assigned to distinct Jr are similar. In other words, thecontextual data does not help in classifying Ja between several Jr.

The quality criteria can be weighted by platform 100. Then, platform 100can average the overall quality as follows:

$\begin{matrix}{{{AvgQuality}\left( {w_{f},w_{k_{h}},w_{c}} \right)} = \frac{\left( {F*w_{f}} \right) + \left( {N*w_{k_{h}}} \right) + \left( {C*w_{c}} \right)}{w_{f} + w_{k_{h}} + w_{c}}} & (5)\end{matrix}$

-   -   where        -   F: Fitness quality        -   N: k_(h) quality        -   C: Contextual Distance quality        -   w_(f): Weight for the quality criteria ‘fitness’        -   w_(k) _(h) : Weight for the quality criteria ‘number of            representatives’        -   w_(c): Weight for the quality criteria ‘contextual Distance’

A weight of 0 skips that criterion. The results can be best if moreweight is given to the fitness quality. Typically, weights w_(l)=5,w_(kh)=1, and w_(c)=1 lead to the best results both for the syntheticdatasets and during our experimentation with a real dataset.

Once all the _(JR) from p have been evaluated, platform 100 can rankthem by decreasing quality.

Before creating new _(JR), platform 100 can make sure that a stoppingcriterion is not met. There are three example evaluation criteria theplatform 100 can use to stop: (1) The platform 100 could stop after acertain number of generations. (2) platform 100 could stop when acertain number of generations have been created without improving theaverage quality. (3) platform 100 could stop when a certain qualitythreshold is reached for one of the evaluation criteria. Because it isdifficult to predict the quality level that can be reached, platform 100might not stop using a threshold. For this reason, platform 100 can usea combination of approaches.

Once the stopping criteria have been evaluated, there are two outcomes.Either one of the criteria is met and the platform 100 stops, returningthe best J_(R) (algorithm 1, line 14), or, platform 100 generate newcandidates by recursively calling the function nextPopulation of thealgorithm 1 (line 21). How platform 100 generates new candidates isdescribed herein.

Once all the journey maps have been evaluated, platform 100 can rankthem by their average quality and copy a fraction (i.e., e) of the bestones. Because platform 100 can keep an untouched version of the e numberof J_(R)s, platform 100 can make sure that the overall quality will onlyincrease or stay steady. Indeed, as can be seen in FIG. 18 , the averagequality (i.e., black line) is never decreasing.

Then, platform 100 can generate (p−e) new J_(R)s as follows. Theplatform 100 can pick one random J_(R) from elite, and perform one ormultiple operations. The platform 100 can define four differentoperators: (1) add a journey, (2) delete a journey, (3) add atouchpoint, and (4) delete a touchpoint. The way they are applied isdetermined using algorithm 2. In extreme cases, platform 100 mightexecute each of these four operators three times. In any case, platform100 perform at least one operation (enforced by line 2 in Alg. 2).Typically, in FIG. 16 , J_(R4) have been produced by taking J_(R2) andapplying two operations: (1) removing the touchpoint ‘D’, and (2) addinga journey picked from Top.

A sequence is randomly picked from Top and added to _(JR). For instance,in FIG. 16 , _(JR7) has been produced by taking _(JR5) from Elite andadding a journey from Top.

A random journey is removed from _(JR). Nothing happens if _(JR)contains only one journey.

A touchpoint from Tis added to one of the journeys from J_(R) at arandom position. For instance, in FIG. 5 , _(J) _(R6) has been producedby adding the touchpoint ‘D’ at the second position of _(J) _(R5) .

A touchpoint is removed from _(J) _(R) unless removing this touchpointwould result in an empty set of touchpoints. FIG. 36 shows theapplication of the genetic operations.

As described in FIG. 15 , once new J_(R)s have been created, platform100 can go back to the evaluation phase where the new J_(R)s areevaluated until one stopping criterion is met. If such a criterion ismet, platform 100 can return the best J_(R)s of the last generation(e.g., _(J) _(R7) in FIG. 16 ), putting an end to the execution.

In order to evaluate the quality of the approach to return the best setof representative journeys in JR, platform 100 can evaluate the resultsusing a collection of synthetic datasets. The platform 100 can generatethe dataset. Then, using this synthetic dataset, platform 100 canevaluate and compare to summarize sequences of categorical data. In thisfirst evaluation, platform 100 does not leverage contextual data toallow for a fair comparison that consider only the sequence ofactivities. Finally, platform 100 can add contextual data to show thegain in information the platform 100 can get from leveraging such data.

To evaluate the results of the approach, platform 100 can produceseveral event logs that simulate journeys. The platform 100 can generatethe event logs using the ground truth represented by the generativejourneys and recover these journeys from a set of actual ones itproduces. A generative journey is a known sequence of activities with aknown set of characteristics from which we generate the event logs.Similar to J_(a), a generative journey Jg is a tuple (S, C), containinga nonempty and ordered set of touchpoints, S, and a set of contextualdata, C. Let J_(G) be a set of k_(G) generative journeys used togenerate a dataset composed of 1,000 actual journeys. Let σ_(G)={S:S∈J_(g), J_(g)∈J_(G)}; i.e., σ_(G) is the set of all the sequencesdefined in the generative journeys.

If platform 100 were to use only these generative journeys to generate1,000 thousand journeys, platform 100 would obtain only k_(G) distinctjourneys. For instance, if platform 100 uses j_(g1)={

ABC

, {age-range:range1}} and j_(g2)=(

ABBD

, {age-range:range2}) to generate 1,000 journeys equally distributed, wewill obtain j_(a)={j_(g1) ⁵⁰⁰,j_(g2) ⁵⁰⁰}. From a business point ofview, this would describe an ideal situation where each group ofcustomers behaves in an homogeneous way. A more realistic situationwould depict a scenario where each group of customers can be describedby a representative sequence of activities, but the actual journeyswithin the group can deviate from the representative one. To producemore realistic data, platform 100 can inject noise for a fraction of thejourneys. For instance, if the noise level is set to 50%, J_(a)=J_(g) istrue for half of the data. For the other half, algorithm 3 describes hownoise is added. Since the noise is added to the two components of thejourney, there are two parts: the sequence of touchpoints and thecontextual data. FIG. 21 illustrates how six journeys 2104 are generatedfrom two generative journeys 2102. If we assume that the noise level isdefined to be 50%, three actual journeys in the events logs deviate fromthe original generative journeys. FIG. 37 shows operations for addingnoise to the sequence of activities (e.g. event traces)

The goal of experiments is to retrieve the set of generative journeys,as representatives, from the produced actual journeys. The noisier thedataset is, the more difficult it is to retrieve the generative journeysfrom the actual journeys. However, the noise is not the only source ofcomplexity. Hence, to ensure complexity-wise diverse datasets, platform100 can define three additional sources of complexity:

(1) Complex number of touchpoints: The more touchpoints are contained inthe generative journeys, the more difficult it is to retrieve them. Wedefine that a journey map possessing 20 or more touchpoints isconsidered complex.

(2) Complex contextual data: The more similar the contextual data isbetween generative journeys, the more difficult it will be todistinguish them. Let c be the average contextual distance (defined inSect. 4.4.3) when there is no noise. We define that if c 0.25, weconsider the journey as complex.

(3) Complex distribution: If the journeys in the event logs are not welldistributed among the generative journeys, it will also be harder toretrieve the original generative journeys. We define that if thestandard deviation of the distribution (normalized to 1) is equal to orgreater than 0.25, we consider the journey as complex. Typically, if 300journeys are generated from J_(g1) while 700 journeys are generated fromJ_(g2), we would consider the journeys as complex (i.e., the standarddeviation is 0.283).

The binary combination of these three sources of complexity results ineight different datasets, which are described in FIG. 38 . As can beseen, the first row (i.e., #0) describes a configuration with 4generative journeys (column ‘kG’), a total of 17 touchpoints, acontextual distance of 0.17, and a standard distribution of 0.17 (column‘Distribution’). Hence, we do not consider the row #0 as complex for anyof the three complexities mentioned above (column ‘Complex’). Combiningthese 8 configurations with 5 levels of noise produces 40 datasets as anexample experiment.

To evaluate and compare the quality of representative journeys, platform100 can use both external and internal evaluation metrics. On the onehand, the external ones evaluate the results relative to the groundtruth, i.e., from the generative journeys. On the other hand, theinternal evaluation uses cluster analysis techniques to assess theresults. The aim is to account for the fact that the ground truth mightnot be the optimal solution. Indeed, adding random noise might changethe optimal solution.

An example metric for External Evaluation is Distance in Number ofJourneys. This metric measures the distance between the number ofgenerative journeys and the number of representative journeys returnedby the algorithm. The platform 100 can use the following metric:NbJourneysDistance(k _(G) ,k _(R))=abs(k _(G) −K _(R))  (6)

An example metric for External Evaluation is Jaccard Distance. Toevaluate the distance between the sequences of activities from thegenerative journeys (σ_(G)) and the discovered representative journeys(σ_(R)), platform 100 can use the Jaccard Distance where a score of 1indicates a perfect match between the set of sequences from thegenerative journeys and the representative ones.

$\begin{matrix}{{{JaccardDistance}\left( {\sigma_{\mathcal{R}},\sigma_{\mathcal{G}}} \right)} = {1 - \frac{{\sigma_{\mathcal{R}}\bigcap\sigma_{\mathcal{G}}}}{{\sigma_{\mathcal{R}}\bigcup\sigma_{\mathcal{G}}}}}} & (7)\end{matrix}$

An example metric for Internal Evaluation is Mean distance. The meandistance i returns the average distance between the representativesequence i and the sequence of actual journeys that have been assignedto i. If the mean distance i is 0, then the representative journey iperfectly matches the underlying actual journeys.

$\begin{matrix}{{MeanDistanceScore}_{i} = \frac{\sum\limits_{j = 1}^{k_{i}}{D\left( {S_{i},S_{ij}} \right)}}{k_{i}}} & (8)\end{matrix}$

where

-   -   D(x₁,x₂): Levensthein distance between two sequences.    -   k_(i): Number of actual journeys assigned to the representative        journey i    -   S_(i): Representative sequence i    -   S_(ij): Sequence of actual journeys j assigned to i

An example metric for Internal Evaluation is Coverage. The coverageindicates the proportion of actual journeys that are within theneighborhood n of a representative.

$\begin{matrix}{{Coverage_{i}} = \frac{\sum\limits_{j = 1}^{k_{i}}\left( {{D\left( {S_{i},S_{ij}} \right)} < n} \right)}{k_{i}}} & (9)\end{matrix}$

where

-   -   D(x₁,x₂): Levensthein distance between two sequences    -   k_(i): Number of actual journeys assigned to the representative        journey i    -   S_(i): Representative sequence i    -   S_(ij): Sequence of actual journeys j assigned to i

An example metric for Internal Evaluation is Distance gain [10]. Thedistance gain measures the gain in using a representative journeyinstead of the true center of the set (i.e., the medoid of the wholedataset). In other words, it measures the gain obtained in usingmultiple representative journeys instead of a single one.

$\begin{matrix}{{DistGain_{i}} = \frac{{\sum\limits_{j = 1}^{k_{i}}{D\left( {{C\left( \sigma_{\mathcal{A}} \right)},S_{ij}} \right)}} - {\sum\limits_{j = 1}^{k_{i}}{D\left( {S_{i},S_{ij}} \right)}}}{\sum\limits_{j = 1}^{k_{i}}{D\left( {{C\left( \sigma_{\mathcal{A}} \right)},S_{ij}} \right)}}} & (10)\end{matrix}$

where

-   -   D(x₁,x₂): Levensthein distance between two sequences    -   k_(i): Number of actual journeys assigned to the representative        journey i    -   S_(i): Representative sequence i    -   S_(ij): Sequence of journeys j assigned to i    -   C(x): True center of the set

The platform 100 can evaluate two settings of the genetic algorithmcompared to techniques that are used to cluster and summarize sets ofsequential and categorical data.

FIG. 22 depicts the approach at a high-level. As can be seen, withtraditional approaches, a first step is to build a distance metric. Thiscan use the edit distance with a constant cost operation set to 1. Oncethe distance matrix is built, this can involve creating k clusters.There can be a test using from 2 to 12 clusters and use the squaredCalinski-Harabasz index to return the most statistically relevant. Then,we return the best representatives of each cluster using theneighborhood density, the centrality, the frequency, or the likelihoodusing the package Traminer. These techniques do not use the contextualdata. Hence, to allow for a fair comparison, we compare these techniqueswith a version of our genetic algorithm that does not use contextualdata. We call this version Genetic₁. We also test our genetic algorithmwith a version that considers the contextual data, called Genetic₂. Notethat both the traditional and genetic approaches use the same techniquesto find k_(h) (i.e., the squared Calinski-Harabasz index) and thedistance is measured using the edit distance with a constant costoperation set to 1. Due to the non-deterministic nature of the geneticalgorithm, we run it ten times for each setting.

The platform 100 can present the results using the external evaluationmetrics, then the internal ones. FIG. 23 shows that the solution thatreduces the Jaccard distance the most is Genetic₂. This is notsurprising, as this solution uses more information (i.e., the contextualdata). Next, the best solution that does not use contextual data isGenetic₁. The drop in the average absolute distance in the number ofjourneys from the ground truth (which is visible in FIG. 24 ) might bewhy the genetic algorithm outperforms the other techniques that solelyrely on the Calinski-Harabasz index to determine the ideal number ofjourneys.

The internal evaluation of FIG. 25 shows that not only does the geneticalgorithm outperform the traditional approaches, it also proposes abetter solution than the ground truth. This can be explained by the factthat when platform 100 injects noise, its potentially changes thesolution.

FIG. 26 summarizes the results.

Experiments Using Real Datasets

This section reports on the experiments with a real dataset, the goalbeing to illustrate how a change in the settings impacts the results.Overall, the main settings are the same as those described in theprevious section. That is, the distance between sequences is measuredusing the edit distance with a constant cost operation set to 1, k_(h)is tested from 2 to 12 clusters using the squared Calinski-Harabaszindex, p is 100, and e is 5. The parameters we adjust are the weightgiven to the fitness, the number of representatives, and the contextualdistance.

We used a publicly available dataset describing the activities performedthroughout the day by Chicago's citizens. There are 15 types ofactivities, such as, ‘being at home’, ‘attending class’, ‘goingshopping’, or ‘doing households errands’. In the context of thisdataset, a journey is the sequence of activities starting from themorning until the night. Typically, ‘being at home’→‘attendingclass’→‘being at home’ is a journey consisting of three activities. Thetotal number of journeys is 29,541 and there are 123,706 activities(with an average of 4.817 activities per journey). This dataset isinteresting not only for the relatively large number of data pointsdescribing life trajectories, but also because of the available detailedcontextual data, such as information on the citizens' demographics.

As a preprocessing step, we binarized the age of the customers into fivegroups (see the legends in FIG. 27 ) and we removed all other contextualdata. The goal was to show the influence of the settings on the journeymap. FIG. 27 shows the results using three different configurations.

In configuration 1, we did not leverage the contextual data (i.e., thecontextual distance weight is set to 0).We interpret the resultingjourney map as follows. The first journey represents people going to‘work’, going back ‘home’ at noon, and returning to ‘work’ in theafternoon. The second journey is close to the first one, the maindifference being that people do not seem to go back ‘home’ at noon. Thethird journey shows citizens being at ‘home’, going ‘shopping’ twice inthe afternoon, and going back ‘home’. Interestingly, the activity‘shopping’ appears twice in a row. Note that this does not necessarilymean that the activity is repeated twice in a row; it might be thatthere are some other activities in between, but because they are notsignificant enough, they are not shown.

In configuration 2, we test the effect on the resulting journey map whenconsidering the ages of the customers. Therefore, we changed the weightput on the contextual distance from 0 to 1. As can be seen in FIG. 16 ,three representative journeys were generated. Each of these journeys hasthree touchpoints. They start from ‘home’ and finish at ‘home’. Inbetween, the first journey has the activity ‘work’, the second one hasthe activity ‘shopping’, and the last one the activity ‘attendingclass’. It is interesting to note the effect of the configuration on thecontextual data (the distribution charts on the right side of FIG. 16 ).Indeed, while the age was equally distributed for each journey inconfiguration 1, we can observe that the age is discriminant inconfiguration 2. For instance, more than half of the citizens in thejourney j₃ are under 16 years old, while this population represents only8.7% of the entire dataset.

In configuration 3, we show the effect when we increase the weight puton the contextual distance parameter. Journeys j₁ and j₃ are identicalto those in configuration 2. However, a new and rather complex journeyj₂ emerges. It starts from being at ‘home’, goes to ‘health care’, andthen alternates between the activities ‘entertainment’ and ‘home’. Ascan be expected, the effect of the contextual data is stronger than inconfiguration 2. We also observe that the distribution is impacted whengiving more weight to homogeneity. We interpret the result as follows:Citizens younger than 29 years old tend to have two typical patterns ofactivities involving either ‘school’ or ‘entertainment’ while the mosttypical journeys for the other citizens involve ‘work’.

Of course, this is a simplified overview of the data. For the almost30,000 actual journeys in the event logs, there are numerous uniqueactual journeys that differ from the representative journeys we get fromthese three configurations. By letting the user choose the weight foreach parameter, we let them explore different perspectives of the data.We claim that the best parameters depend on the dataset, the businesscontext, and the goal of the exploration.

The platform 100 can use different quality criteria to guide theevolution process of discovering the best representative journeys for agiven set of actual journeys that otherwise would be unreadable on adisplay device or interface. The platform 100 can perform well onsynthetic datasets and that they allow us to discover journey mapsshowing different alternative perspectives of the same real dataset.However, quality criteria might be created to fit a business context.Typically, the criterion simplicity might be used in addition to or toreplace k_(h). Because it is difficult to anticipate what will be theideal number of clusters returned by k_(h), simplicity would offer amore intelligible way to choose between a highly fitting but morecomplex journey map, or a less fitting but simpler journey map.Industry-driven quality criteria might be used in addition to the onesproposed.

For the contextual distance, platform 100 can limit the set ofcontextual data to categorical data and—in the experimentation using areal dataset—platform 100 can select one item of contextual datamanually (i.e., the age). The platform 100 can also preprocess the datato distribute the age into bins (e.g., 60+ years old) to turn thecontinuous feature into a categorical one. Datasets can possess numerousitems of contextual data of different types.

Finally, a genetic approach is one example solution to solve thecustomer journey discovery task.

The genetic approach to summarizing a set of customer journeys with thepurpose of displaying them on a journey map offers an interestingalternative. First, the quality of the results is better, which is trueusing both internal and external evaluation metrics. Second, the weightsof the three quality criteria are a flexible way to analyze a datasetunder different perspectives. Third, in addition to the sequence ofactivities, our genetic algorithm can leverage contextual data to groupsimilar journeys. By doing so, platform 100 can provide a way tosummarize insights from customers that are hidden in the data.

The platform 100 can generate an interface to guide end-user in theprocess of making abstract traces from complex ones by leveraging theknowledge about the control-flow of activities stemmed from processmodels (produced by process mining discovery algorithm.

A trace or map is a conceptual tool used to visualize typical customers'trajectories when using a service. In their simplest form, maps show theinteractions between a customer and a service provider through time. Aseries of interactions is called a journey. Because maps give a companya better understanding of their customers, they are becomingincreasingly popular amongst practitioners.

A map can anticipate the best—or worst—journeys possible. Such journeys,displayed on a map, are called the expected journeys. However, customersmight experience a different journey from the one anticipated. For thisreason, platform 100 can leverage traces left by customers ininformation systems to build maps from evidence. Because the journeysthat will be displayed on the map are produced from facts, they can bereferred to as actual journeys.

However, when dealing with numerous journeys, it becomes unrealistic todisplay all the actual journeys on a single map at interface application130. For illustration purposes, FIG. 28 depicts 10,000 instances of thetraces related to the handling of reviews for a journal, an examplesynthetic dataset. In the context of this dataset, the service provideris the conference's organizing committee, the customers are theresearchers submitting their papers, and a journey describes thehandling of the reviews, from the submission until the final decision.In FIG. 28 , at 2802 it is difficult to apprehend the typical paths ofthe reviewing process. To this end, representative journeys have beenintroduced as a means of reducing the complexity. Indeed, the centralmap 2804 uses two representative journeys to summarize 10,000 actualjourneys.

Although representative journeys decrease the complexity by reducing thenumber of journeys, a map might still be difficult to apprehend when itis composed of many activities. Indeed, even though only representativejourneys are being used, quickly spotting the main differences betweenthe two journeys visible in 2804 (FIG. 28 ) is not straightforward dueto the high number of activities and the length of the journeys.

The platform 100 can implement a map abstractor that leverages theexpertise of process discovery to abstract maps for visualrepresentation. More precisely, platform 100 can take as an input aprocess tree, parse it, starting from the leaves, and iteratively askthe end-user if it is relevant to merge the activities that belong tothe same control-flow, and, if so, to provide a name for this group ofactivities. By doing so, platform 100 can let the end-user decide whichactivities should be merged and how they should be renamed. Then, onecan visualize the same maps at different levels of granularity using aslider, which is visible in FIG. 28 at 2806. At a certain level ofgranularity, platform 100 can enable an end user to observe, given theend activities, that one representative journey summarizes the acceptedpapers, while the other one depicts the rejected papers. The mapabstractor can explore a seamless integration of business process modelswith journeys maps.

The platform 100 can implement process mining and process discoveryactivity. Section The platform 100 can implement customer journeydiscovery activity.

The platform 100 can implement integration of Process Mining withJourney Mapping and showcases the impact that the latter can have in theanalysis of journeys.

Process mining involving machine learning, data mining and processmodeling and analysis. The platform 100 can implement the discovery ofprocess models, one of the three types of process mining along withconformance and enhancement.

The idea behind the discovery of process models is to leverage theevidence left in information systems to build process models from eventlogs. The resulting process models are, therefore, based on factualdata, showing how the process was really executed. To build such amodel, process mining uses an input data format called event logs. Anevent log is a collection of traces, a trace being a single execution ofa process composed of one or multiple activities.

For illustration purposes, let T=(

bdcef

;

acdefg

;

bcdefgg

) be an event log composed of 3 traces and 7 distinct activities.Regardless of the notation, the resulting models can express thecontrol-flow relations between activities. For instance, for the eventlog, T, the model might express the following notation: 1) A and B arein an XOR relation (X); i.e., only one of them is executed; 2) C and Dare executed in parallel (+); i.e., both activities are executed in anyorder; 3) E and F are in a sequence relation (→); i.e., F always followsE; 5) G is in a XOR loop (Combination of X and

); i.e., it can be executed 0 or many times. Note that T denotes asilent activity. It is used to correctly execute the process but it willnot result in an activity which will be visible in the event logs. FIG.29 displays the five aforementioned relations using a process tree.

Discovering a process model from event logs is a challenge. The platform100 can be robust enough to generalize (to avoid overfitting models)without being too generic. The platform 100 can build process models orprocess trees.

A process tree is an abstract hierarchical representation of a processmodel, where the leaves are annotated with activities and all the othernodes are annotated with operators such as X. Process trees are a meansof guaranteeing the soundness of the discovered models. A model isconsidered to be not sound when some activities cannot be executed orwhen the end of the process cannot be reached.

The soundness guarantee is one reason that we choose the process treenotation. There are also three other reasons. First, process models inblock structure achieve best performance in terms of fitness, precision,and complexity. Second, the hierarchical structure of process trees isideal to derive multiple levels of granularity. Finally, process treescan be used by top-performing process model algorithms, such as theinductive miner or the Evolutionary Tree Miner [6].

The platform 100 can use a process mining based model that allows us tomap a standard event log from process mining (i.e., XES) to storecustomer journeys, as an attempt to bring customer journeys and processmining closer together.

Discovering a set of representative journeys that best describe theactual journeys observed in the event logs is a challenge inspired bythe process discovery challenge. However, instead of describing thecontrol flows of activities using a business process model, the maintrajectories (i.e., the representative journeys) can be shown using ajourney map. It encompasses example challenges: (1) choosing the numberof representatives. Let k be this number of representative journeys usedon a journey map; (2) grouping actual journeys in k clusters; and (3)for each k, finding a representative journey. The platform 100 canovercome them.

First, the number of representative journeys used to summarize theentire actual journeys needs to be set. Looking at 2802 from FIG. 28 ,it is difficult to say how many representative journeys should be usedto summarize the data. The platform 100 can use different ways to findthe number of representative journeys. First, the number ofrepresentative journeys can be set manually. It can also be set usingstandard model selection techniques such as the Bayesian InformationCriterion (BIC) penalty or the Calinski-Harabasz index, for example.

Once k has been defined, actual journeys should be split in k clustersand a representative journey per cluster must be found. One of the ways,is to first define a distance function between actual journeys, such asthe edit distance, or shingles, and to build a distance matrix; then, tosplit the actual journeys in k groups using hierarchical clusteringtechniques. Next, the representative can be found using a frequentsequence mining algorithm, by counting the density of sequences in theneighborhood of each candidate sequence, by taking the most frequentsequences, or by taking the medoid. Instead of inferring therepresentative from the distance matrix, it is also possible to obtainit using statistical modeling. The platform 100 can employ anExpectation-Maximization algorithm on a mixture of k Markov models, andthen for each Markov model the journey with the highest probabilitybecomes the representative.

The platform 100 implements map abstractor using different steps torender a map at different levels of abstraction. The steps 300 aredepicted in FIG. 30 . In the first step, the goal is to build a processtree given an event log. Next, using the same event log, the goal is tobuild a journey map.

The third step consists of parsing the tree obtained in step 1. To thisaim, the platform 100 can use a script (stored in memory 108) whichparses the process tree (i.e., XML file) and performs a reverseBreadth-first search; i.e., traversing the operators in the tree fromthe lowest ones to the root in a level-wise way. Let l be the number ofoperators in the process tree. At each of the l operators of the processtree, platform 100 offers the opportunity to the end-user to merge theleaves under the operator. If the user chooses to merge the activities,she should provide a new name and the operator virtually becomes a leaf.If the end-user chooses not to merge the activities, platform 100 cankeep the leaves intact. If the answer is no, platform 100 can keep theactivities separated at all levels of granularities, and we also disablethe parents' steps. Indeed, platform 100 can postulate that if a userdoes not want to merge two activities at a low level of granularity, itdoes not make sense to merge them later at a higher Level ofgranularity.

Finally, in step 4, platform 100 can transform the journey map atdifferent levels of abstraction. Let λ be the number of abstractionswhich will be available for a journey map. It can be seen as the numberof steps that will be included in the sliders visible in FIG. 28 at 2806Note that λ is equal to the number of times the end-user decides tomerge the activities and that λ=l when the end-user merges all theactivities. Let operator λ be the λ^(th) operator to be merged. LetGetLevelAbstraction (journey map, λ, pt) be a function that returns ajourney map at the λ^(th) level of abstraction. Algorithm 1 shows howthe function Abstract is recursively called to get to the level ofabstraction λ. The parameter removeSeqRepeats in Algorithm 1 in line 7emphasizes that continuous sequence of activities that are to bereplaced, will be replaced by only one instance of the new name givenfor this operator. For instance, if the journey is “AABCBAC”, the leavesthat are to be replaced, are “A” and “B” and the new name is “X”, thejourney will become “XCXC”. This reduces the length of the journeys and,thus, increases the abstraction. FIG. 40 shows example operations forgetting to the level of complexity.

This section provides a running example of the tool implemented byplatform 100. The running example is based on synthetic event logsdescribing the handling of reviews for a journal referred to here. Itcontains 10,000 journeys and 236,360 activities.

In the first step, we obtained a process tree by using the inductiveminer with default parameters. It results in the process tree 3100visible in FIG. 31 .

In the second step, platform 100 can obtain a journey map by: (1)measuring the distance between actual journeys using the edit distance;(2) building a dendrogram using a hierarchical clustering algorithm; (3)finding k using the Calinski-Harabaz Score (k=2); (4) findingrepresentative journeys which results in a journey map which is visiblein 2804 (FIG. 28 ).

In the third step, platform 100 can parse the XML in javascript. Totraverse the tree, platform 100 can be using a tree-like datastructures. The order in which the operators are parsed is depicted inFIG. 31 (i.e., ‘step’). FIG. 31 shows that we decided to merge 7 out ofthe 9 operators. Note that we decided not to merge the activities‘reject’ and ‘accept’, which disabled the option of merging all theactivities below step 9. FIG. 32 shows a screen capture 3200 of theapplication when merging the activities during step 1.

Finally, FIG. 33 shows the resulting journey maps at three levels ofabstraction 3302, 3304, 3306.

Journey maps are being used more and more to help service providers putthemselves in their customers' shoes. The platform 100 providesautomated ways of building them and displaying complex paths on ajourney map. By leveraging process trees—a format build within theprocess mining community—platform 100 can bring customer journeyanalytics and process mining closer together.

The following discussion provides many example embodiments of theinventive subject matter. Although each embodiment represents a singlecombination of inventive elements, the inventive subject matter isconsidered to include all possible combinations of the disclosedelements. Thus if one embodiment comprises elements A, B, and C, and asecond embodiment comprises elements B and D, then the inventive subjectmatter is also considered to include other remaining combinations of A,B, C, or D, even if not explicitly disclosed.

The embodiments of the devices, systems and methods described herein maybe implemented in a combination of both hardware and software. Theseembodiments may be implemented on programmable computers, each computerincluding at least one processor, a data storage system (includingvolatile memory or non-volatile memory or other data storage elements ora combination thereof), and at least one communication interface.

Program code is applied to input data to perform the functions describedherein and to generate output information. The output information isapplied to one or more output devices. In some embodiments, thecommunication interface may be a network communication interface. Inembodiments in which elements may be combined, the communicationinterface may be a software communication interface, such as those forinter-process communication. In still other embodiments, there may be acombination of communication interfaces implemented as hardware,software, and combination thereof.

Throughout the foregoing discussion, numerous references will be maderegarding servers, services, interfaces, portals, platforms, or othersystems formed from computing devices. It should be appreciated that theuse of such terms is deemed to represent one or more computing deviceshaving at least one processor configured to execute softwareinstructions stored on a computer readable tangible, non-transitorymedium. For example, a server can include one or more computersoperating as a web server, database server, or other type of computerserver in a manner to fulfill described roles, responsibilities, orfunctions.

The technical solution of embodiments may be in the form of a softwareproduct. The software product may be stored in a non-volatile ornon-transitory storage medium, which can be software product includes anumber of instructions that enable a computer device (personal computer,server, or network device) to execute the methods provided by theembodiments. The embodiments described herein are implemented byphysical computer hardware, including computing devices, servers,receivers, transmitters, processors, memory, displays, and networks. Theembodiments described herein provide useful physical machines andparticularly configured computer hardware arrangements.

Although the embodiments have been described in detail, it should beunderstood that various changes, substitutions and alterations can bemade herein.

Moreover, the scope of the present application is not intended to belimited to the particular embodiments of the process, machine,manufacture, composition of matter, means, methods and steps describedin the specification.

As can be understood, the examples described above and illustrated areintended to be exemplary only.

I claim:
 1. A computer system for generating visual elements of a userinterface application, the computer system comprising: a data storagedevice for storing event traces, each event trace having attributes thatindicate activities over time; a processor configured to process machineexecutable instructions to generate visual elements for the userinterface application by: generating a plurality of hierarchicalclusters for the plurality of event traces based on a genetic processfunction, each hierarchical cluster in the plurality of hierarchicalclusters comprising a set of event traces and a representative eventtrace based on representative attributes of the set of event traces, thegenetic process function mapping each event trace to a hierarchicalcluster; generating the visual elements for the user interfaceapplication, the visual elements indicating the plurality ofhierarchical clusters and, for each hierarchical cluster, therepresentative event trace based on the representative attributes, therepresentative event trace summarizing the set of event tracescorresponding to the hierarchical cluster; controlling rendering of theuser interface application at a device to display the visual elementsand a plurality of selectable portions; and responsive to a selection ofa selectable portion of the plurality of selectable portions,controlling rendering of the user interface application at the device tonavigate the plurality of hierarchical clusters.
 2. The system of claim1 wherein the processor is further configured to implement the geneticprocess function by: evaluating initial set representative event tracesto generate elite set representative event traces; generating additionalinitial representative event traces using a transformation process;evaluating the additional initial representative event traces togenerate additional elite set representative event traces; continuingthe generating and the evaluating until stopping criterion is met; andusing resulting elite representative event traces as the representativeevent traces for the plurality of hierarchical clusters.
 3. The systemof claim 1 wherein, for each event trace, the attributes are representedas a two-dimensional tuple of activities over time and contextual dataand the genetic process function can evaluate the two-dimensional tuplesof the event traces.
 4. The system of claim 1 wherein the processorcomputes, for each hierarchical cluster, the representative event traceas a pattern of activities to summarize the patterns contained withinthe set of event traces of the hierarchical cluster.
 5. The system ofclaim 1 wherein the processor uses contextual information to determinethe representative event trace.
 6. The system of claim 1 wherein theprocessor uses one or more neural networks to process event traceattributes.
 7. The system of claim 1 wherein the processor implementsthe genetic process function by: pre-processing the event traces;determining an initial population that corresponds to a number of theplurality of hierarchical clusters; evaluating initial representativeevent traces; evaluating stop criteria; implementing a genetic operationto generate additional initial representative event traces; andoutputting a map abstraction for use in generating additional visualelements.
 8. The system of claim 7 wherein the processor is configuredto evaluate the initial representative event traces based on qualitycriteria for fitness, a number of representatives, a contextual distanceand an average quality.
 9. The system of claim 7 wherein evaluating thestop criteria further comprises at least one selected from the group of:stopping after a pre-determined number of generations; stopping after apre-determined number of generations have been created without improvingthe average quality; and stopping once a threshold has been reached forone of the evaluation criteria.
 10. The system of claim 8 wherein theprocessor is configured to rank the initial representative event tracesbased on each representative event trace's average quality.
 11. Thesystem of claim 8 wherein the processor is configured to weigh thequality criteria to generate an overall quality and average the overallquality.
 12. The system of claim 1 wherein the processor is configuredto evaluate the initial representative event traces based on internaland external evaluation metrics.
 13. The system of claim 12 wherein atleast one of the external evaluation metrics is the Jaccard distance.14. The system of claim 12 wherein at least one of the internalevaluation metrics is the mean distance between representative eventtraces and the set of event traces.
 15. A system for generating visualelements of a user interface application comprising: a data storagedevice for storing event traces, each event trace having attributes thatindicate activities over time, and each event trace relating to currentdata and historic data; a processor configured to process machineexecutable instructions to generate visual elements for the userinterface application by: generating a mapping of process miningelements to the event traces, the mapping having an XML-based set ofconcepts, the mapping linking process de facto models and de jure modelsto actual and expected customer experiences; generating one or morerepresentative journeys using a genetic process function, the one ormore representative journeys stored in a data structure for hierarchicalcomponents of a journey map, each component having an element and anattribute, the journey map having at least one element being a root nodeof the hierarchical components, the actual and expected customerexperiences corresponding to the journey map; generating visual elementsfor the components of the journey map, wherein the visual elementscorrespond to the one or more representative journeys; and transmittingthe visual elements to the user interface application for display on adevice.
 16. The system of claim 15 wherein the processor uses neuralnetworks to generate tools for process mining.
 17. The system of claim15 wherein the user interface application is configured to explore aplurality of customer journeys simultaneously.
 18. A system forgenerating visual elements of a user interface application comprising: adata storage device for storing event traces, the event traces havingattributes that indicate activities over time; a processor configured toprocess machine executable instructions to generate visual elements fora map abstractor of the user interface application by: generating aprocess tree for the event traces by grouping the event traces based onsimilar attributes, the process tree having nodes corresponding to theevent traces, the process tree having leaf nodes; parsing the processtree; starting from leaf nodes of the parsed process tree, iterativelygenerating a prompt at the user interface application to merge a set ofevent traces that belong to a subset of the parsed process tree; uponreceiving confirmation to merge the set of event traces, generating aname for the set of event traces and replacing the nodes correspondingto the set of event traces with a new node indicating the name and a setof representative attributes for the set of event traces; generating thevisual elements for the user interface application, the visual elementsindicating the process tree and the new nodes, the visual elementsrepresenting an abstracted process tree; and controlling rendering ofthe user interface application at a device to display the visualelements.
 19. The system of claim 18, wherein a process tree is anabstract hierarchical representation of a process model, where theleaves are annotated with activities and all the other nodes areannotated with operators.